Systems and methods for calibrating, configuring and validating an imaging device or system for multiplex tissue assays

ABSTRACT

A system and method for characterization and/or calibration of performance of a multispectral imaging (MSI) system equipping the MSI system for use with a multitude of different fluorescent specimens while being independent on optical characteristics of a specified specimen and providing an integrated system level test for the MSI system. A system and method are adapted to additionally evaluate and express operational parameters performance of the MSI system in terms of standardized units and/or to determine the acceptable detection range of the MSI system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/004,244, filed Jun. 8, 2018, entitled “SYSTEMS AND METHODS FORCALIBRATING, CONFIGURING AND VALIDATING AN IMAGING DEVICE OR SYSTEM FORMULTIPLEX TISSUE ASSAYS,” which claims the benefit and priority of U.S.patent application Ser. No. 14/764,918, filed Jul. 30, 2015, which is aU.S. National Stage filing of International Patent Application No.PCT/EP2014/051920, filed Jan. 31, 2014, which claims the benefit andpriority under 35 U.S.C. 119(e) of U.S. Provisional Patent ApplicationNo. 61/759,262, filed Jan. 31, 2013, the entire contents of each of theabove patent applications are incorporated herein by reference for allpurposes and are considered part of this disclosure.

TECHNICAL FIELD

The present invention relates to systems and methods for calibration ofimaging devices. More specifically, the present invention involvescalibrating a multispectral imaging system and/or components thereof.The present invention also involves configuring operational parametersof the imaging system

SUMMARY OF THE INVENTION

Embodiments of the invention provide for a method for assessing thequality of a multispectral imaging (MSI) system that includes aprocessor programmed to govern an operation of said imaging. Embodimentsof the invention also include computer-implemented methods forcalibrating, characterizing, and configuring an MSI. Such methodcomprises collecting data, during a first spectral scan of the MSIsystem across at least at least a portion of a spectral range of the MSIsystem, and at an output of a detector of the MSI system and with noexposure of said detector to ambient light, such as to form a first setof spectral data representing output of said detector at chosenwavelengths. The method additionally includes determining presence ofstray light in the MSI system by comparing subsets of said acquiredspectral data; and optically adjusting the imaging system when thepresence of stray light is positively determined.

During a second spectral scan of the MSI system across said at least aportion of a spectral range of said MSI system, receiving, at thedetector, light from a first light source that has standardized outputpower and a spectrum of a calibrated light standard to form a second setof spectral data representing output of the detector at the chosenwavelengths. Moreover, the method further includes a step of receiving,at the detector and during a third spectral scan of the MSI systemacross said at least a portion of a spectral range of the MSI system,light from the first light source to form a second set of spectral datarepresenting output of said detector at the chosen wavelengths.

Alternatively or in addition, the method involves determiningoperational characteristics of the MSI system in terms of standardizedunits. Such determination may involve one or more of determining a slopeof a curve representing a mode intensity of an image acquired with theMSI system on an intensity variance of said image at one or more singlewavelengths or narrow bandwidths; calculating noise figure associatedwith data acquisition by the MSI system; and determining awavelength-dependent response of the MSI with the use of incident lighthaving a spectrum containing multiple spectral bandwidths ofsubstantially equal widths centered at wavelengths corresponding toemission wavelengths of a known spectral marker. The known spectralmarker optionally includes at least one of a chosen analyte and aquantum dot.

Embodiments of the invention further provide a system for calibratingand determining the performance of a multispectral imaging (MSI) system.In one implementation, such system includes (i) at least one lightsource configured to operate with substantially fixed operationalcharacteristics and including an optical filter having a transmissionspectrum that corresponds to a spectrum of a calibrated light standardand (ii) a geometrical standard characterized by distribution ofreflectivity that is spatially-periodic. The operational characteristicsof the light source include at least temperature and electricaloperational characteristics, and the light source is adapted to producelight output with spectrum including multiple spectral bands centered atrespectively corresponding central wavelengths. The MSI systemadditionally includes an optical system configured (a) to receive saidlight output from the used or active light source, (b) to deliver lightfrom said received light output to said geometrical standard, and (c) toredirect light that has interacted with said geometrical standard tosaid MSI system. The optical system is optionally configured to redirectlight that has reflected from said geometrical standard. Light powerdelivered from the light source to the geometrical standard can,optionally, be varied independently from variation of the transmissionspectrum. Furthermore, in one embodiment the light source is configuredto deliver, to the geometrical standard, a first beam of light thattransmits through said geometrical standard and a second beam of lightthat reflects from the geometrical standard such that spectral bandsassociated with the first light and spectral bands associated with thesecond light substantially overlap.

In one embodiment, the system for calibration is configured such as topermit adjustment of light power, in a given spectral band selected frommultiple spectral bands, that is directed to the MSI system withoutsubstantially affecting spectral content of the other spectral bands.The system for calibration may be further configured such as to permitmeasurement of light power, in a first spectral band selected from themultiple spectral bands, substantially independently from measuring oflight power in a second spectral band selected from the multiplespectral bands.

Embodiments of the invention additionally provide a system forcalibration of performance of a multispectral imaging (MSI) system thathas an object plane and a field of view (FOV). Such system forcalibration includes at least one light source adapted to produce lightoutput having a spectrum with multiple bands such that amount of lightin one or more of the multiple bands is adjustable substantially withoutaffecting a remaining spectral band, while each of the multiple bands iscentered at a corresponding central wavelength. The system forcalibration further includes an optical system defining multiple opticalpaths for illumination of the object plane and configured to deliverlight from the object plane, to the MSI system. Such system forcalibration is adapted to permit determination of light power in a firstspectral band, selected from the multiple spectral bands, substantiallyindependently from determination of light power in a second spectralband selected from the multiple spectral bands. The optical system ofthe system for calibration is configured, in one embodiment, to gatherlight that has interacted with the geometrical standard in bothreflection and transmission.

In a specific embodiment, the system for calibration additionallycontains a reference sample configured, when placed at the object plane,to spatially separate light in a spectral-band dependent fashion such asto permit spatial calibration of optical performance of the MSI systemacross the FOV. The system for calibration may further include aprocessor, programmed to form a set of data representing amount of lightcarried in each of the multiple spectral bands, and tangiblenon-transitory computer-readable medium operably connected to theprocessor and adapted to store such set of data.

Embodiments of the invention also provide a method for determiningaccuracy and precision of a computational algorithm for spectralunmixing of a multispectral (MS) image. The method includes (i)acquiring, with a detector, image of a reference sample evenlyilluminated or substantially evenly illuminated with light from a lightsource having spectral output with multiple spectral bands; (ii)correcting the acquired image for the baseline intensity offset of pixelvalues or ‘bias’ to form a bias-corrected MS image; and (iii)determining an integrated intensity value based on an averaged intensityprofile corresponding to said bias-corrected acquired image.

In one implementation, the determining of an integrated intensity valueincludes averaging a spectral profile of intensity of the bias-correctedMS image over chosen pixels of the detector such as to form an averagedintensity profile. Alternatively, or in addition, the step of acquiringmay include acquiring an image of a reference sample illuminated withlight from a light source, which light source contains an optical filterhaving a transmission spectrum corresponding to a spectrum of acalibrated light standard. Alternatively or in addition, the step ofinquiring may include (a) receiving, with the detector, a first beam oflight that has transmitted through the reference sample and a secondbeam of light that has reflected off of the reference sample, where eachof said first and second light has a corresponding multiband spectrum;and (b) determining a contribution, to light received with the detector,of light in a first spectral band of the first beam of light, where thesuch determination is carried out independently from the determinationof a corresponding contribution of light in the second spectral band ofthe second beam of light.

In a specific implementation, the method may include a step of varyingrelative contributions of light from different spectral bands to imageacquired with the detector, where such process of varying is performedsubstantially without changing spectral content of light received withthe detector. In addition, the method may include defining relativecontributions of light from different spectral bands of an output of thecalibrated light source; and individually normalizing averaged intensityprofiles corresponding to the multiple spectral bands to definenormalized individual reference spectra respectively corresponding tothe multiple spectral bands. The method optionally also includes a stepof determination of differences between results of the computationalspectral unmixing algorithm and the defined relative contributions oflight.

Embodiments of the invention alternatively provide a method fordetermining a wavelength dependence of operation of a multispectralimaging (MSI) system, which method includes the steps of (i) acquiring,with a detector of the MSI system, first image data representing animage of an object illuminated with first wavelength or narrow bandwidthof light from a light source that has output spectrum with multiplespectral bands; (ii) acquiring, with the detector, second image datarepresenting an image of the object illuminated with second wavelengthor narrow bandwidth from the light source, such that the first andsecond light correspond to different first and second spectral bands ofthe multiple spectral bands and have respectively corresponding firstand second power; and (iii) determining normalized quantum efficiency atdifferent wavelengths for the detector. The method may further include astep of (iv) collecting third image data, representing an image of theobject illuminated with third wavelength or narrow bandwidth from thelight source, with the use of the determined normalized quantumefficiency, such that the third light corresponds to a third spectralband of said multiple spectral bands, and the third spectral band isdifferent from the first spectral band.

Embodiments of the invention also include a method of calibrating aspectral camera of a multispectral imaging (MSI) system comprising:illuminating a substrate with a light source of a first predeterminedintensity level and/or power a first time; collecting a first set ofspectral image data of the substrate via a sensor of the MSI system;illuminating the substrate with the light source at the firstpredetermined intensity level a second time; collecting a second set ofspectral image data of the substrate via a sensor of the MSI system atthe first predetermined intensity level; and subtracting or adjustingthe first set of spectral image data from the second set of spectralimage data, and generating first difference image data; collecting athird set of spectral imaging data at a second predetermined intensitylevel and a fourth set of spectral imaging data at the secondpredetermined intensity level; subtracting the third set of spectralimage data from the fourth set of spectral image data, and generatingsecond difference image data; calculating at least one of the mode andthe mean of the first difference image data; determining at least one ofvariance and standard deviation of pixel values of the first differenceimage data, based on the at least one of the mode and the mean of thefirst difference image data at every wavelength of the first differenceimage data, generating first resulting image data; calculating at leastone of the mode and the mean of the second difference image data;determining at least one of variance and standard deviation of pixelvalues of the second difference image data, based on the at least one ofthe mode and the mean of the second difference image data at everywavelength of the second difference image data, generating secondresulting image data; generating a conversion value, for eachwavelength, based on the first resulting image data, the secondresulting image data, the at least one of the mode and the mean of thefirst difference image data, and the at least one of the mode and themean of the second difference image data, wherein the conversion valueis representative of an approximate number of electrons recorded at eachpixel per grey level. The conversion value is determined by generating aslope or approximate slope between (1) a set of data corresponding tothe first resulting image data as a function of the at least one of amode and the mean of the first difference image data and (2) a set ofdata corresponding to the second resulting image data as a function ofthe at least one of a mode and the mean of the second difference imagedata. The conversion value for each wavelength is compared to the otherconversion values for each wavelength, and wherein differences betweenthe values are utilized to calibrate the MSI system.

Embodiments of the invention also include a method for generating acorrected image for a multispectral imaging system, comprising: A methodfor generating a corrected image for a multispectral imaging system,comprising:

acquiring a first spectral image via a sensor when the exposure time ofa first spectrum source of the system is zero, and generating firstspectral image data at a plurality of wavelengths; determining a modalpixel intensity value for each wavelength of the plurality ofwavelengths of the first spectral image, wherein the modal pixelintensity value at each wavelength of the plurality of wavelengths ofthe first spectral image corresponds to a pixel intensity offset valueat each wavelength of the plurality of wavelengths of the first spectralimage; acquiring a second spectral image by the first spectrum source,and wherein the exposure time of the first spectrum source is greaterthan zero, and generating second spectral image data of a plurality ofwavelengths; and subtracting the pixel intensity offset value at eachwavelength of the plurality of wavelengths of the first spectral imagefrom a value of each of a plurality of pixels at each correspondingwavelength of the second spectral image data.

In exemplary embodiments of the present invention, mean values may bereplaced by modal values, or other suitable values.

Embodiments of the present invention may also involve a method ofcalibrating a spectral camera of a multispectral imaging (MSI) system,said method comprising: illuminating a substrate with a light source ofa first predetermined intensity level a first time; collecting a firstset of spectral image data of the substrate via at least one of a sensorof the MSI system and the spectral camera; illuminating the substratewith the light source at the first predetermined intensity level asecond time; collecting a second set of spectral image data of thesubstrate via the at least of a sensor of the MSI system and thespectral camera; and subtracting the first set of spectral image datafrom the second set of spectral image data, and generating firstdifference image data; collecting a third set of spectral imaging datavia the at least of the sensor of the MSI system and the spectral cameraat a second predetermined intensity level; collecting a fourth set ofspectral imaging data at the second predetermined intensity level;subtracting the third set of spectral image data from the fourth set ofspectral image data, and generating second difference image data;calculating at least one of the mode and the mean of the firstdifference image data; determining at least one of variance and standarddeviation of pixel values of the first difference image data at everywavelength of the first difference image data, based on the at least oneof the mode and the mean of the first difference image data, andgenerating first resulting image data; calculating at least one of themode and the mean of the second difference image data; determining atleast one of variance and standard deviation of pixel values of thesecond difference image data at every wavelength of the seconddifference image data, based on the at least one of the mode and themean of the second difference image data, and generating secondresulting image data; generating a conversion value for each wavelengthof the second difference image data based on the first resulting imagedata, the second resulting image data, the at least one of the mode andthe mean of the first difference image data, and the at least one of themode and the mean of the second difference image data, wherein theconversion value is representative of an approximate number of electronsrecorded at each pixel per grey level in at least one of the first,second, third, and fourth spectral image data.

In exemplary embodiments of the present invention, the light source, maybe replaced by another spectrum source, and the light or spectrum sourcemay also remain activated or on, such that, for example, when two setsof spectral image data are captured at the same predetermined intensitylevel or power, the substrate is illuminated once, and thus, there is noneed to illuminate the substrate a second time.

Embodiments of the invention also include a computer program productwhich, when loaded on a non-transitory tangible computer-readable, andoptionally programmable, medium, is configured to program a computerprocessor to effectuate steps of the disclosed invention, including theabove-mentioned methods and operation of the above-mentioned systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood by referring to thefollowing Detailed Description in conjunction with the Drawings, whichare generally drawn not to scale and of which:

FIG. 1A is an example of a multispectral image acquired with a typicalmulti-spectral imaging (MSI) system.

FIGS. 1B and 1C are schematic illustrations of typical MSI systems.

FIGS. 2A-2C are block-schemes illustrating the principle of linearunmixing.

FIG. 3 is a graphical scheme showing comparison of advantages anddisadvantages of using quantum dots and chemical fluorophores as markersfor quantitative multiplexing.

FIG. 4A is a schematic of an embodiment of an illumination channel of aMSI system containing a first calibration source of light including acalibrated light emitter and a calibration optical filter.

FIG. 4B is a graph representing spectral distribution of intensity ofthe first calibration source of light of FIG. 4A.

FIG. 5A is a block diagram of an exemplary embodiment of an imagingsystem, in accordance with the present invention.

FIG. 5B illustrates one embodiment of a method for measuring theintensity of light transmitted at locations within an imaging system, inaccordance with the present invention.

FIG. 5C is a flow chart representing a method for determining thespectral image offset correction data and/or image.

FIG. 5D is a flow chart illustrating a method for correcting spectralimages for intensity offset, in accordance with an embodiment of thepresent invention.

FIG. 5E is a flow chart illustrating method 600 for determining astandardized conversion (mean/variance calculation) of spectral imagesfrom arbitrary grey-scale units to standardized intensity units (e−).

FIG. 5F is a flow chart illustrating method 700 for determining theelectronic noise associated with spectral data acquisition in terms ofstandardized intensity units.

FIG. 5G is a flow chart illustrating method 800 for evaluating thedynamic range of a spectral imaging sensor.

FIG. 6A is a flow chart illustrating method 1200 for determining thelinear response of the spectral imaging apparatus to linear increases inillumination FIG. 6B is a plot representing spatially averaged spectraldata imaged of a calibration illumination source acquired at 4 lightlevels

FIG. 6C is a graph representing gain and linearity characteristics ofthe MSI system determined at a single spectral wavelength from a datasetaccording to the embodiment of the method of FIG. 6 .

FIG. 7A is a spectral distribution of intensity of light generated bythe calibrated light emitter of the source of light of FIG. 4Aillustrating the location of Hg elemental peaks in the spectra.

FIG. 7B is a graph showing traces of the normalized spectraldistribution of FIG. 7A acquired with different spectral resolution.

FIG. 7C is a method, in accordance with an embodiment of the presentinvention, for characterizing the spectral features of an image or imagedata to a known set of spectral features associated with an object.

FIG. 7D is a method 1400 for verifying spectral resolution, inaccordance with an embodiment of the present invention.

FIG. 8A is a method 1500 utilized to assess spectral-spatial coordinateaccuracy, in accordance with an embodiment of the present invention.

FIGS. 8B and 8C show the respective intensity profile and image of asample precision standard used with an embodiment of the invention todetermine spatial accuracy and precision of imaging provided by a MSIsystem.

FIG. 9A is an exemplary method of determining the quantum efficiency ofspectral detection, in accordance with the present invention.

FIG. 10 is a flow-chart representing a method for verification of aprocess of spectral unmixing of the relative contributions from themultiple spectral peaks of light output from a calibrated source oflight.

FIGS. 11 and 12 are spectra graphs illustrating the embodiment of themethod of FIG. 10 .

FIG. 13 shows normalized reference spectra for individual spectral bandsof the spectrum of FIG. 4B.

FIG. 14 is a bar-chard showing results of linear unmixing test performedwith the use of normalized reference spectra of FIG. 13 .

FIG. 15A is a schematic of an embodiment of a two illumination channelMSI system containing first and second calibration sources of light.

FIGS. 15B and 15C are spectral graphs showing, in comparison, normalizedspectra of the first and second source of FIG. 15A.

FIGS. 16A and 16B are spectral graphs illustrating a method forverification of a process of spectral unmixing of the relativecontributions from the multiple spectral peaks of light output frommultiple calibrated sources of light according to an embodiment of theinvention.

FIG. 17 shows normalized reference spectra for individual spectral bandsof the spectra of first and second sources of light of FIG. 15B, 15C.

FIG. 18 is a plot representing the spectral trace registered by thedetector of the optical acquisition system of the invention employingtwo calibration/reference light standards.

FIG. 19 illustrates the use of area under the aggregate spectral traceof FIG. 18 . FIGS. 20A, 20B, and 20C provide an illustration to asystem-level test of a measurement system that is assumed to have beenpre-calibrated.

FIGS. 21A-1 through 21A-3 and 21B-1 through 21B-3 provide plots andrelated data illustrating a spectral unmixing of 9 spectral featuresFIG. 22 illustrates the results of operation of a related embodiment ofthe invention.

FIG. 23 is a block diagram of an exemplary computing system in whichdescribed embodiments can be implemented.

DETAILED DESCRIPTION

Embodiments of the present invention may be employed with an imagingsystem such as a multispectral imaging (MSI) system (for example, animaging spectrometer, a fluorescent microscopy system, a pathologyimaging system). MSI systems, generally, facilitate the analysis ofpathology specimens, including tissue samples. MSI systems typicallyinclude, for example, computerized microscope-based imaging systemsequipped with spectrometers, spectroscopes, spectrographs, spectralcameras, charge couple devices (CCDs), light sensors, optical detectors,and/or imaging spectrometers etc.). MSI systems and/or devices are ableto capture the spectral distribution of an image at a pixel level, andprovide the ability to acquire multispectral data representing atwo-dimensional (2D) spatial field of view, with data sets representinglight intensity as a function of wavelength at each pixel of an imagerecorded by an optical detector.

While there are various multispectral imaging systems, an operationalaspect that is common to all MSI systems is a capability to form amultispectral image such as that schematically presented in FIG. 1A, forexample. A multispectral image is one that contains image data capturedat specific wavelengths or at specific spectral bandwidths across theelectromagnetic spectrum. These wavelengths may be singled out byoptical filters or by the use of other instruments capable of selectinga pre-determined spectral component including electromagnetic radiationat wavelengths beyond the range of visible light range, such as, forexample, infrared (IR).

Two common types of MSI systems facilitating the acquisition of imagesof a specimen are schematically and generally illustrated in FIGS. 1Band 1C. FIG. 1B shows a system 100 including an imaging system 104, forexample an optical imaging system, a portion 108 of which contains aspectrally-selective system that is tunable to define a pre-determinednumber N of discrete optical bands. The imaging system 104 is adapted toimage an object, for example, a tissue sample 110, transmitting,absorbing or reflecting illumination from a spectrum source 112, such asa broadband light source or other source of radiation onto a detector116 (e.g., optical detectors, light sensors, image sensors, CCDs,photodetectors, photosensors, spectral camera, etc.). In an exemplaryembodiment, the detector 116 is included in the imaging system 104. Asshown, in FIG. 1B, the imaging system 104, which in one embodiment mayinclude a magnifying system such as, for example, a microscope having asingle optical axis 120 generally spatially aligned with an opticaloutput 122 of the imaging system 104. The imaging system 104 formsimages of the object 110, for example, a sequence of images of theobject 110 as the spectrally-selective system 108 is being adjusted ortuned (for example with a computer processor 126) such as to assure thatimages are acquired in different discrete spectral bands. The system 100may additionally contain a display 122 in which appears at least onevisually-perceivable image of the tissue from the sequence of acquiredimages. Alternatively, the display 122 is a touch screen display. Thespectrally-selective system 108 may include an optically-dispersiveelement such as a diffractive grating, a collection of optical filterssuch as thin-film interference filters or any other system adapted toselect, in response to either a user input or a command of a processor126 (which may be a pre-programmed processor), a particular pass-bandfrom the spectrum transmitted from the spectrum source 112 through theobject 110 towards the detector 116.

An alternative implementation 150 of a system adapted to simultaneouslytake a multiplicity of spectrally-discrete optical images in severalspectral bands is shown in FIG. 1C. Here, the spectrally-selectivesystem 154 defines several optical outputs corresponding to N discretespectral bands. The system 154 intakes the transmitted light output 156from the imaging system 158, (e.g., an optical system) and spatiallyredirects at least a portion of this light output, simultaneously, alongN spatially different optical paths 162-/ through 162-N in such a way asto image the sample 110 in an identified spectral band onto a detectorsystem 166 along an optical path corresponding to this identifiedspectral band. It is appreciated that another alternative embodiment(not shown) may combine features of the embodiments 100 and 150. The useof such spectral imaging devices for fluorescence microscopy enableshigh-value diagnostics of various samples (for example, biologicaltissues) using fluorophores, such as multiplexed nucleic acid andprotein markers.

As shown schematically in FIG. 2 , the spectral data produced by suchinstrumentation can be decomposed into different acquisition portions or“analyte channels” 210 that represent the relative contributions ofdifferent analytes or fluorophores 214 used with the sample to theacquired overall emission spectrum. FIG. 2 provides illustration to theprinciple of linear unmixing (also sometimes termed “spectraldeconvolution” or “spectral decomposition”). According to thisprinciple, the spectral data of the original spectral data cube such asthat of FIG. 1A is computationally compared to known reference spectraof, for example a particular analyte; and then the linear unmixingalgorithm is used to separate the known spectral components intochannels' that represent the intensity contribution (e.g., the netintensity) of each analyte at each pixel. Such analyte-specificinformation is useful, for example, for interrogating relative analyteconcentrations and can provide a new depth of information for diagnosisand/or prognosis of a particular disease and its status by a physician.The useful result of interrogation comes from separation of spectraldata representing molecules and/or markers of interest from that causedby background light such as background and/or noise fluorescence (forexample, from fluorescent metabolic byproducts) and backscattered light.Accordingly, the abilities to acquire high-resolution spectral imagedata, and unmix or deconvolve mixed spectral contributions to such datacaused by different sources of light, is also important for removingcontributions of constitutive autofluorescence. The increased signal tonoise ratio afforded by spectral imaging better enables accuratedetermination of localization of a source of light or spectrum ofinterest in space (referred to as signal localization) that relates todetermination of the anatomy of the tissue at hand.

The use of quantum dots spectral markers offers a number of advantagesfor multiplex assay technology (FIG. 3 ). The emission spectra ofquantum dots are well approximated with narrow spectral distributionshaving substantially symmetric intensity profiles. This propertyfacilitates the process of spectral distinction of the quantum dots fromother sources of light used as emitting probes or markers. Selections ofa multitude of quantum dot species that emit in different spectralranges across the visible spectrum can be used for multiplexed tissuediagnostics. The emission spectrum of a given quantum dot species istypically defined by physical size of the quantum dot. Because emissionspectrum is determined by physical size of the quantum dot, the emissionspectrum will not be susceptible to wavelength shifts due to changes,for example, in the chemical or solvent environment in the tissue withwhich the quantum dots are associated. The excitation spectrum of aquantum dot is rather broad for the majority of the quantum dot emissionspecies and extends well into the UV range. As a result, multiplequantum dot species have overlapping excitation spectra. The resultingpossibility of excitation of multiple quantum dot species with radiationwithin the region of overlapping excitation spectra, for example, with anarrow-band light (substantially a single wavelength that is wellseparable from the emission spectra of the same quantum dot species), isadvantageous because it enables straightforward control of the quantumdot-excitation procedure. Specifically, it allows ensuring thatsubstantially the same amount of excitation light is delivered all toanalytes of the sample. Quantum dots are also known as substantiallyphotostable species.

The abovementioned excitation characteristic of quantum dots differsfrom that of chemical fluorophores. In contrast to quantum dots,different chemical fluorophores emitting at different wavelengthstypically require excitation at different wavelengths of the visiblespectrum. For that reason, using chemical fluorophores as markers withbiological tissue may complicate the excitation process. In particular,the use of multiple chemical fluorophores associated, as markers, withthe tissue requires a multi wavelength excitation scheme. In addition,it becomes non-trivial to ensure that contributions of differentmultiple chemical fluorophores to the overall multiplexed emissionspectrum accurately reflect relative concentrations of chemicalfluorophores used with the tissue as spectral markers.

A schematic comparison of specific characteristics of spectral detectioninvolving quantum dots and chemical fluorophores/dyes is provided inFIG. 3 . Spectral properties of chemical dyes, such as broad emissionbands, narrow absorption spectra, and susceptibility to photobleachingare drastically different from those of the quantum dots, which havenarrow emission bands, broad absorption spectra, and strong resistanceto photobleaching. As a result, methods of calibration ofimage-acquiring instrumentation designed for quantum dot quantumdot-based imaging are poorly adapted to image acquisition based onchemical dye fluorescent standards with the use of the same equipment.In practice, confirmation of accuracy of the measurement is difficult toachieve because such accuracy depends on the use of samples withanalytes 1) of know concentrations; 2) that are photostable; and/or 3)have properties consistent with the experimental samples.

Commercially available fluorescent standards for calibration ofimage-acquisition equipment are typically associated with and/oradsorbed to beads designed for use with flow cytometry. For example,depending on a system of optical filters used with an image-acquisitionsystem, results of the spectral unmixing analysis of the emissionspectrum obtained with the use of such chemical markers may often becomesimply irreconcilable with standard calibration specifications of thesystem. The use of beads may, in some cases, complicate obtaining alarge sample size per field (which would otherwise increase thesignal-to-noise ratio, SNR, in the measurements). Large beads mayproduce a lens-like effect due to their curved geometry and/orcontribute to the same image from different object planes.

Therefore, in order to precisely and reliably use standards inmulti-analyte spectroscopy, and to ensure consistent and accurate dataacquisition from the tissue specimen, and to permit accurate assessmentof relative contributions of the analytes to the overall emission data,such calibration of the multi-analyte MSI system at a system level isrequired that is not currently provided for. The unmet need arises, inpart, because of the lack of appropriate calibration standards. Inaddition, parameters of computational spectral deconvolution or unmixingalgorithms used to process the image data acquired with such MSI systemmust also be properly configured and confirmed to produce results thatreflect actual spectral distributions. Thus, it is important to specify,for example, dynamic ranges for the development of both a measurementsystem and staining assay(s).

This also calls for development of methods for reliable verification ofthe results of a spectral unmixing image-data processing. The unsolvedproblem that this application is addressing is, therefore, at leastfour-fold: (i) to devise system(s) and method(s) for characterizationand/or calibration of performance of such imaging system that permit(s)the use of the system with a multitude of different fluorescentspecimens (i.e., to effectively decouple the performance of the imagingsystem from being linked to the use of a specified specimen); (ii) toprovide a test of the spectral performance of the whole MSI system (anintegrated system level test); and (iii) to evaluate and expressoperational parameters performance of the MSI system in terms ofstandardized units and (iv) to determine the acceptable stainingdetection range that must be met to ensure performance according tospecifications.

The integrated system level tests are important, for example, in 1)validating unmixing performance of an algorithm, for example, an imageanalysis algorithm and/or a system involving multiplexed quantum dotreporters, and 2) may be tailored to reflect quantum dot emissionwavelengths for a plurality (for example, 6 or 7 or 8 or more) analytesacross the visible spectrum and into the IR range. The systems andmethods proposed below, unlike conventional testing methods that expressrelative intensities as arbitrary units, facilitate interpretation ofthe analyte channel and raw data intensity information in terms ofstandardized intensity units (SIU) and, therefore, permit meaningfulcomparisons of intensity data from different instruments. The ability toexpress both signal and noise (or other operational characteristics) interms of standardized units permits meaningful specification andcomparison of SNRs of imaging data acquired with the use of differentMSI systems under standardized conditions and enables the comparison ofoperational performance of different instruments. This advance provides,for example, the ability to define the dynamic range limitations indefined measures of instrument performance, and to isolate instrumentdynamic range from the dynamic range of fluorescent signalingtechnology.

Components of an exemplary embodiment of an image acquisition system 400in accordance with the present invention are shown in FIG. 4A. Theexemplary image acquisition system 400 includes a spectrum source 410,for example, a light source. In an exemplary embodiment of the presentinvention, the spectrum source 410 is configured to include a spectrumemitter, having well-defined spectral properties (e.g., an Hg-lamp,xenon or other arc lamp, laser lines, luminescent radioactive standards,chemiluminescent standards, phosphors, and/or LEDs) The power andtemperature of the spectrum source 410 may be stabilized and monitoredwith closed loop electronic circuitry and/or a multi-bandpass filter 410a. The multi band-pass filter 410 a has n predefined pass-bands and ispositioned in front of the spectrum source 410. In exemplary embodimentsof the present invention, a spectrum acquisition system 442, forexample, a microscope based light acquisition device, includes or iscoupled to a spectral camera 443. The spectrum acquisition device 442includes a scanning platform 445 that moves along an axis, for example,along an x and/or y axis, and is utilized to scan an object (which maybe placed on a platform), such as slide and/or biological specimen, suchthat an image of the object can be captured.

According to an embodiment of the invention shown in FIG. 4A, the imageacquisition system 400 includes a first spectrum source 410, that isconfigured to provide spectrum, for example, excitation light, havingspectral characteristics defined by the spectrum source 410. Inexemplary embodiments of the present invention the spectrum source 410is a broadband light source (for example, having an emission spectrabetween 350-nm and 700-nm) used for fluorescent imaging applications. Inan exemplary embodiment of the present invention, the spectrum source410 a self-calibrating light source, and the power and temperature ofthe light source is stabilized and monitored with a closed-loopelectronic feedback circuitry.

The image acquisition system 400 also includes a spectrally selectivesystem 410 a, (e.g., a multi-bandpass filter 410 a which has npredefined pass-bands and is positioned in front of the spectrum source410). In one embodiment, the spectrally selective system 410 isconfigured to ensure that transmission of light between any two of itsadjacent pass-bands is substantially blocked (for example, reduced by atleast 3 orders of magnitude as compared to the highest transmissionlevel of the filter). Consequently, light 414, which that is produced bythe source 410, may pass through a chromatically neutral mechanism 416,for example, an iris diaphragm 416 of the spectrum source 410, andimpinge onto the beamsplitter 418 (such as, for example, a 50/50beamsplitter), and has a predetermined calibration spectrum 422, asshown in FIG. 4B. By utilizing, for example, the spectrally selectivesystem 410 a, the spectral properties and power of the spectrum (e.g.,light) 414, such as the intensity and wavelength of the spectrum 414that will impinge on the sample 430, can be determined before thesample/object 430 is placed in the path of the radiation orillumination. In exemplary embodiments of the present invention, theiris diaphragm 416, located at the pupil plane, is opened or closed, tovarious degrees, to vary the spectrum 414 output from the spectrumsource 410.

A portion of light 414 passes through an optical system 436 (such as alens system having at least one lens) and forms an incident beam 426.Incident beam 426 then reaches a first side 447 of the object 430, forexample, a partially reflective and partially transmissive (i.e.,transflective) substrate, such as, a microscope slide, after passingthrough the optical system 436.

Light 440 reflected from the object 430 is received and detected by acomponent of the MSI system (for example, the spectral camera 443) aftertraversing a filter 444, such as a neutral density filter. In anexemplary embodiment of the present invention, the filter is an ND3filter, identified as part no. XB27/25R and manufactured by OmegaOptical of Vermont. The filter 444 is utilized to attenuate intensity ofmeasured light to reduce it to levels comparable to the intensity levelsconsistent with fluorescent samples. In a related embodiment, the imageacquisition system 400 may have a second spectrum source 448, on theopposite side of the object 430, for example a transmissive light sourcethat generates a beam 446 having its own spectrum, that is incident ontoa second side 449 of the object/sample 430, such that the spectrum fromthe second spectrum source 448 passes through the object/sample 430towards the spectrum acquisition device 442. The second spectrum source448 may be an alternative to the spectrum source 410, or may be providedas an additional spectrum source.

Shown in FIG. 5A, is a block diagram of another exemplary embodiment ofan imaging system 500, in accordance with the present invention. In anexemplary embodiment of the present invention, the imaging system 500 isa spectral imaging system that includes an image acquisition apparatus502, such as a spectral camera having sensors 504 that receive light. Inan exemplary embodiment of the present invention, the image acquisitionapparatus 502 is included in a scanner 506. The system 500 includes animage forming apparatus 508 coupled to the image acquisition apparatus502. In exemplary embodiments of the system 500, the image formingapparatus 508, for example, (1) includes at least one lens 510; (2) isan optical train; and/or (3) is a microscope. An object positioningapparatus 512 is coupled to the image forming apparatus 508. Inexemplary embodiments of the present invention, the object positioningapparatus 512 is utilized to position an object, for example, a slide,for obtaining single images or scanned images. In an exemplaryembodiment of the present invention, the object positioning apparatus512 is, for example, a microscope stage that is part of a microscope. Inexemplary embodiments of the present invention, the object positioningapparatus 510 may move in at least one of an x-direction, y-direction, az-direction, a rotational direction, and an angular direction.

The system 500 and/or each of the systems' components (e.g., imageacquisition apparatus 502, the image forming apparatus 508, and theobject positioning apparatus 512 may be controlled by a single CPU 514.It should be understood by one skilled in the art that a CPU 516, 518,520 may, alternatively or additionally, be included in or coupled to anyone of the components of the image acquisition apparatus 502, the imageforming apparatus 508, and/or the object positioning apparatus 512,respectively.

A first spectrum source 522 provides spectrum, such as light, for thesystem 500, and, in an exemplary embodiment of the present invention,delivers spectrum to a plane 524 of the object positioning apparatus512. In an exemplary embodiment of the present invention, the spectrumsource 522 may include a control unit 526 that is utilized to control,select or enter the desired spectrum output wavelength or wavelengthrange of the spectrum source 522. In an exemplary embodiment of thepresent invention, the first spectrum source 522 is a self-calibratingsource (i.e., a source having its own sensor that monitors and helps toregulate the spectrum output), such as a self-calibrating light sourceidentified as part number P010-00201R, manufactured by Lumen Dynamics ofOntario, CA (city and state). In an exemplary embodiment of the presentinvention the spectrum source 520 is coupled to the image acquisitionapparatus 502. In an exemplary embodiment of the present invention, aspectrally selective system, such as spectrally selective system 528,may be placed in the path of the spectrum source 522. The system 500 mayalso include a second spectrum source 530, for example, a transmissionlight source that illuminates a side of an object, which is placed onthe object positioning apparatus 512, on a side opposite to the side ofthe object receiving incident spectrum from the first spectrum source522. In an exemplary embodiment of the present invention, a spectrallyselective system, such as spectrally selective system 528, may be placedin the path of the spectrum source 530. In an exemplary embodiment ofthe present invention, the second spectrum source 530 may include acontrol unit 532 that is utilized to control, select or enter thedesired spectrum output wavelength or wavelength range of the spectrumsource 530. In an embodiment of the present invention, the spectrumcontrol unit 526,532 is any device or method that regulates the outputof the spectrum source 410, and may include filters. In an exemplaryembodiment of the present invention, the spectrally selective system 528may be external to the spectrum source 522,530. In an exemplaryembodiment of the present invention a spectrum control unit 526,532includes a meter or sensor. In an exemplary embodiment of the presentinvention, the spectrum control unit 526,532 regulates the output ofspectrum from the spectrum source 522, 530 before it traverses theimaging system 500, or components thereof (such as, the image formingapparatus 508 (e.g., optical train)). A sensor or meter 534 is utilizedto sense, measure and/or characterize spectrum provided to the system500, by the first and/or second spectrum sources 526, 530, at any pointin the system 500. In an exemplary embodiment of the present invention,the sensor or meter may be coupled to any computer or CPU that isinternal or external to the system 500, e.g., CPUs 514, 516, 518, and520.

An input device 536 is coupled to the CPU 512. In an exemplaryembodiment of the invention, the input device 536 is a keyboard, mouse,touch pad, or other input device. In exemplary embodiments of thepresent invention, any or all of the CPUs 514, 516, 518,520 may beconnected to a network 538. One or more servers 540,542 and/or storagedevices 544,546 may be connected to the network 538 and/or any one ormore of the CPUs 514, 516, 518,520. While the devices, apparatusesand/or components of the system 500 are described as part of the system500, the apparatuses, devices and/or components of system 500 may standalone or be coupled to the system 500 by a wireline or wirelessconnection.

Referring now to FIGS. 5A and 5B we now describe methods of calibratinga system in accordance with the present invention, for example, theimaging system 500 and/or components of the system 500. Calibration ofsystem 500 may involve, for example, measuring an amount of spectrumintensity, at any location in the system 500 for example, measuringillumination, at or near the object plane 524. The intensity of spectrumoutput by the spectrum source 522,530 may not match, for example, theamount of spectrum incident at the object plane 524. By ascertaining theamount of spectrum incident at the object plane 524, one can repeatedlydeliver that same amount of spectrum (e.g., light) to the object plane524 (e.g., the site of a tissue sample on a slide). Thus, by identifyingthe amount of illumination that reaches the object plane 524, anoperator of the system 500 is able to standardize an amount of spectrum,for example light, delivered to one or more objects, such as biologicalspecimens, placed at or near the object positioning apparatus 512.

FIG. 5B illustrates one embodiment 550 of a method for identifying theintensity of spectrum at locations within the system 500. This methodmay be performed for every desired spectrum output, for example,excitation light wavelength output range by the spectrum source 522. Themethod 550 starts with step 552 in which the spectrum source 522 isturned on, such that spectrum is output from the spectrum source 522. Instep 554, spectrum output may be filtered and/or adjusted (e.g., by thespectrally selective system 528 or control unit 526, such that thespectrum output correlates to a particular wavelength or band, andfiltered and/or adjusted spectrum output is generated.

Steps 554 through 560 may be repeated to measure a characteristic ofspectrum of a second and/or different wavelength or band generated bythe spectrum source 522. In another embodiment of the invention, steps554 through 560 may be repeated to measure a characteristic of spectrumof a second wavelength or band generated from a second spectrum source530. The spectrum wavelength or band of the second spectrum source maybe adjusted or filtered to a same or a different wavelength or band asadjusted or filtered for the first spectrum source 522. The steps ofmethod 550 may be continuously repeated for spectrum output of variouswavelengths. Thus, for example, the intensity of spectrum attributed toone or more wavelengths at a location in the system 500 is identified,and may be used to standardize or calibrate the system 500 to a known orexpected level of performance.

In an exemplary embodiment of the present invention, a spectrallyselective system 528, is placed within the spectrum source 522 or isplaced in the path of the spectrum source 522, and a spectrum amount ismeasured at or near the output of the spectrum source and/or thespectrally selective system 528, to determine the performance ofspectrum source or another component of the system 500 before thespectrum reaches for example, the image forming apparatus 508. Thus, forexample if the intensity or power of spectrum is not what it is expectedto be at the object plane 524, then the component that may be causingthe unexpected delivered spectrum intensity at the particular locationin the system 500 may be more readily identified (e.g., a lens of an theimage forming apparatus may not be meeting its expected performancestandards.

Calibration of the system 500, shown in FIG. 5A, may also involvedetermining the dynamic range, i.e., an approximate minimum and anapproximate maximum of the sensing capabilities of the image acquisitionapparatus 502, for example, a spectral camera, scanner, or componentsthereof, such as the camera's sensors. In an exemplary embodiment of thepresent invention, minimum of the dynamic range is the smallest spectralsignal that is sensed by the camera that is measurably above the totalnoise identified.

The dynamic range is determined by first ascertaining an intensityoffset corrected image and/or pixel offset corrected image data(sometimes referred to a bias image and/or bias image data) without anyinput from the first or second spectrum source, which will be used tocalibrate any images taken subsequent to calibration. FIG. 5C is a flowchart that represents a method 560 for determining the offset value tobe applied to all image data to correct the offset of the intensityvalues due to the camera electronics. In step 562, the system isconfigured so that light is blocked from transmitting to the imagesensor. In step 563, the sensor or camera of the system is set toacquire images with zero exposure time to ensure that no stray light isaccumulated while determining the offset value. In step 564, a spectralimage is acquired with these settings (this is effectively an image of‘nothing’, therefore any intensities that do show up are due toelectronics of the camera). From the image acquired in step 564, themodal pixel value is calculated at each wavelength image to ascertainthe offset of pixel values above zero. This information can be used tosubtract this offset, at each wavelength, from all the pixels insubsequent images. The end result is that pixels that do not receivelight are set to a value of zero. The modal pixel intensity value of theimages captured with no input from the spectrum source is sometimesreferred to the bias image, bias image data, bias offset image, pixeloffset image, pixel offset value, or pixel offset image data, as it isthe pixel offset image, data, and/or value. In exemplary embodiments ofthe present invention, the offset correction value may be expressed inunits of grey-scale value, or electrons (e−) or Coulombs (C), forexample. The offset value is applied to images (e.g. spectral images) orspectral image data (e.g., multispectral image data) when an image islater taken of an illuminated field or object (e.g., a biologicalspecimen on a slide) using the system 500.

FIG. 5D is a flow chart representing a method 570 for determining acorrected image and/or corrected image data, based on pixel offsetcorrection data and/or image. In step 572, a spectrum source isactivated. In step 573, the spectrum output is filtered or adjusted to aspecific wavelength or bandwidth. In step 574, the spectral source isadjusted to an appropriate standardized power output for the imageacquisition. In step 576, the camera exposure time is adjusted to anappropriate value for the intensity level of the light reaching thecamera. In step 577, a first image of an evenly illuminated field iscaptured by the image acquisition apparatus 502. In step 578, the offsetvalue for each wavelength (previously derived in method 560 outlinedabove) is subtracted from every pixel, at each corresponding wavelength,of the acquired spectral image. Steps 573 through 577 may be repeatedusing the same settings to derive a pair of images from whichinter-pixel intensity variance can be calculated, and this process maybe repeated for various wavelengths, wavelength bands, and/or exposuretimes.

Determining the dynamic range may also involve the method 600, shown inFIG. 5E, for determining a standard intensity unit conversion for thegreyscale values reported by a noise (which includes determining meanand variance multi-spectral camera and this value will be used later forexpressing the electronic noise image data) of the imaging system 500,or components thereof (for example the sensors of the spectral camera).In step 602, the spectrum source 522 is activated. In an exemplaryembodiment of the present invention, the system 500 is configured, suchthat the illumination that the imaging acquisition apparatus 502receives is even or substantially even (e.g., the illumination acrossthe sensors of a spectral camera is even). The intensity level is foreach data acquisition to follow is calibrated using a sample planesensor temporarily placed in the object plane to measure theillumination level and adjust to the desired output at a givenwavelength. In step 602, a first image (e.g., spectral/multispectralimage) is captured with the image acquisition apparatus 502 while theone or more spectrum sources (e.g., broadband light source or lightsources) are turned on. In step 602, a second image is captured by theimage acquisition apparatus 502. In step 602 the offset correctionvalue, image, and/or data as identified by the method 570 above, issubtracted from the pixel value at each wavelength of the zero exposureimage from each of the first and second images and/or first and secondimage data to generate a first corrected image and/or first correctedimage data (e.g., an image cube of data, such as the spectrum intensityvalues for each x, y,)\, captured) and a second corrected image and/orsecond corrected image data (e.g., an image cube of data).

In step 603, a resultant difference image data is generated fromsubtracting the corresponding offset-corrected spectrum intensity valuesof first and second images and/or first and second sets of correctedimage data, respectively. In step 604, spatial characteristics, forexample, a standard deviation of the pixel intensity values, for eachwavelength/band in the resultant corrected image data and is furtherused in determining variance values associated with the pixel intensityvalues at each wavelength or band. In step 604, a variance is determined(e.g., based on the standard deviation, such as by dividing themultispectral standard deviation image data by 2) for eachwavelength/band of the resultant difference image data. It should beappreciated by one of ordinary skill in the art that the variance may bedetermined before the standard deviation is determined. It should beunderstood by one of ordinary skill in the art that while the methodsare described by determining, for example, the standard deviation,variance, and mean, the aforementioned (standard deviation, variance,and mean) are related and thus, may suffice to determine and/or replaceone as an alternative for another in the steps of the methods of thepresent invention. Further, the steps of the present invention,involving, for example, determining the standard deviation, variance,and mean may not necessarily need to be performed in the order describedin the methods of the present invention.

In step 605, the mode pixel intensity value at each wavelength of atleast one of the first and second corrected image data is generated,determined, or received, and divided into the variance determined instep 604 for the corresponding wavelength/band of the resultantcorrected image to generate a conversion value for each wavelength. Theresulting conversion value is representative of for example, the numberor an approximate number of electrons recorded at each pixel by a CCDsensor in the spectral camera per grey level. As a result, for example,a level of brightness of an image (e.g., a spectral image) is reflectedin a standardized unit of measurement (SIU), for example electrons (e).A conversion to the SIUs facilitates the expression of the SNR anddynamic range of the camera in terms of standardized units (as a resultof standardized conditions), as well as objective comparison ofmeasurements and/or measurement results between or among differentanalytical and imaging systems. Standardized conditions are thoseconditions where, to a highest degree possible, factors that mayinfluence the measurement are controlled and reported such that themeasurement conditions can be reliably reproduced and/or modeled.

In an exemplary embodiment of the present invention, the noiseassociated with sensor electronics of a data acquisition system isgenerally a primary factor limiting the dynamic range of an MSI systememploying the CCD technology. According to an embodiment of theinvention, the determination of image-acquisition noise involves thefollowing steps illustrated in method 700 (FIG. 5F):

In step 702, first and second spectral offset (or bias) images areacquired, without any light of the system being activated, as similarlyperformed in steps 562 through 564 (FIG. 5C).

-   -   In step 703, a difference image is generated by subtracting the        first offset image from the second offset image. The difference        image represents the isolated noise from sensor electronics        during data acquisition;    -   In step 704, the standard deviation of the difference image is        calculated;    -   In step 705, the adjusted value of the standard deviation (for        the increase in variability due to subtracting the two images)        is corrected or adjusted by dividing the adjusted value by the        square root of two, and generating a resulting value.    -   In step 706, the resulting value is converted to standardized        intensity units by multiplying the resulting value by the        conversion factor determined in step 606 of FIG. 5E. The        resulting value and the converted resulting value correspond to        a determined measure of the acquisition noise of the imaging        system that may be utilized to develop noise specifications.

The dynamic range is sometimes expressed as a ratio of the maximum andminimum light intensity values that the imaging acquisition apparatuscan for example, digitize (i.e., sense and convert the analog signal toa digital signal). In an exemplary embodiment of the present invention,the maximum limit of range is determined by multiply the highest greylevel for a particular bit depth (for example, an image having a depthof 8 bits has a highest greyscale level of 255) by the conversion value.The minimum value is at or near the noise floor is or is approximatelythe conversion value (e.g., the electron conversion value) added to thenoise calculated. FIG. 5G illustrates the flow for determining dynamicrange at every wavelength for a spectral imaging system at standardillumination and exposure settings.

In calibrating the system, the linearity of the sensor response may alsobe determined. Shown in FIG. 6A is a method 1200 for determining thelinearity of the system 500, or components thereof (e.g., the linearityof the response of the imaging apparatus acquisition apparatus 502, suchas a digital camera, spectral camera, or the camera's sensors tospectrum (e.g., light)). The linearity is determined to ascertainwhether the signal output from, for example, the imaging acquisitionapparatus 502 is proportional to the amount of spectrum (e.g., light)received. In an exemplary embodiment of the present invention, theimaging acquisition apparatus 502 is a digital or spectral camera havingcharge-coupled device (CCD) sensors. One of the functions of the CCDsensors is to convert photons carrying image information (i.e., ananalog signal) into an electronic signal (i.e., digital signal).Ideally, the signal output from the imaging apparatus and/or componentsthereof should be linearly proportional to the amount of light incidenton the sensors. In an exemplary embodiment of the present invention, theamount of light incident on the sensors is relative to an amount ofexposure time to spectrum (e.g., light).

FIG. 6A illustrates the method 1200 for determining the linearity of animaging apparatus and/or components thereof (e.g., a spectral cameraand/or the camera's sensors). In an exemplary embodiment of the presentinvention, each image is acquired using a pre-determined exposure time(e.g., 20 ms) at multiple wavelengths (as shown in FIG. 6B, n=4) andpre-determined light levels appropriately distributed across a largepart of the dynamic range of the system 500 (e.g. at 10 mW, 40 mW, 70mW, 100 mW) to obtain spectral data cubes corresponding to differentlevels of incident light, as shown in FIG. 6B. The spectral data is,generally, obtained at wavelength chosen to mimic wavelengths offluorescence of chosen markers. In FIG. 6A, step 1202, a first set ofimage data is captured using method 600 of an uniformly illuminatedfield (e.g., a substrate, such as a slide that is partially reflective)and a mode and variance of the pixel intensity values of the first setof image data is determined. The light level captured by the imagingacquisition apparatus 502 should be set at or near the maximum of thedynamic range of the sensor at a set exposure time. In step 1203, asecond image and/or a second set of image data is captured of an object(e.g., a substrate, such as a slide that is partially reflective) at ornear the minimum of the dynamic range and a mean and variance of thepixel intensity values of the second image and or second set of imagedata is determined. In step 1204, a third image and/or a third set ofimage data is captured of an object (e.g., a substrate, such as a slidethat is partially reflective), by the imaging acquisition apparatus 502,somewhere in between the minimum and maximum of the dynamic range and amode and variance of the pixel intensity values of the third image andor third set of image data is determined. In step 1205, the variancevalue is plotted on the abscissa and the mode value is plotted on theordinate of a graph for each of the three (or optionally more) points.The slope of a straight line fitted to the points represents theconversion value and should ideally be in agreement with the valuecalculated in method 580 (at the given wavelength being evaluated). Themeasure of ‘goodness of fit’ for a straight line to the data points is ameasure of the sensor's linearity of response (at this givenwavelength).

In step 1207, the linear regression is determined for each of the setsof mean and variance data associated with the first, second, and thirdimages and/or set of data at a given wavelength. In an exemplary,embodiment of the present invention, the mean and variance dataassociated with the first, second, and third images and/or set of datamay be plotted on a graph. In an exemplary embodiment of the presentinvention, the linear regression may be determined via a least-squarescalculation:MinQ(variance_(noise),slope)=Σ(variance_(i)−variance_(noise)−slope_(i))²Where i represents a given light level, variance_(noise) represents thevariance calculated for offset images (no light), and the slope_(i)represents the slope at the variance/mode datapoint for a given lightlevel. The equation above yields the slope for a line originating at thevalue of variance calculated for offset images (no light):variance_(estimate)=slope*mode_(i)+variance_(noise)In step 1207, the R² value is determined or identified:

SS_(err) = ∑(variance_(i) − variance  predicted)²SS_(total) = ∑(variance_(i) − variance_(mean of all values))²$R^{2} = {1 - \frac{SSerr}{SStotal}}$Where variance_(predicted) is the variance value predicted by the lineequation at a given light level and variance_(mean of all values) is themean value for the variance values gathered at different light levels.SS_(err) represents the ‘residual sum of squares’ and SS_(total)represents the ‘total sum of squares’ to evaluate the ‘goodness of fit’for the datapoints to the line calculated through the datapoints.

The R² value is indicative of the linearity of the image acquisitionapparatus 502, or component thereof (e.g., the sensors of a spectralcamera). For example, if the R² value is equal to one (1), then thesystem may be regarded as highly linear and ideal for quantitation. Instep 1206, a slope is determined from the equation of a line fit to themean and variance data associated with each of the first, second, andthird images and/or data sets. Ideally, the slope of this fitted linewill not vary greatly wavelength to wavelength. Steps 1204 through 1222is repeated for various wavelengths/bandwidths in the dataset. FIG. 6Cshows the mean intensity vs. variance dependence of a nearly perfectlinearity, assessed using the embodiment 600. The value of standardizedintensity unit per unit grey level, is determined from the slope to beabout 3.3 e for the wavelength λ_(k). A linear regression fitting curvethrough the acquired points of the dependence yields R² value for thechosen wavelength λ_(k). The R² value reflect a degree of linearity ofthe MSI system's (spectrometer's) response, with R²=1 indicating ideallinearity.

The determination of the imaging system's standard unit conversion,dynamic range, and linearity of its performance provides calibrationfoundation for interpreting acquired image intensity information interms of standardized units of e⁻, the range of detectable values thatthe instrument is capable of recording, and the relationship betweenintensity values and the intensity of the sample. The use of these basicmetrics for spectral imaging instruments permits meaningful comparisonsof the intensity data obtained with different instruments.

Spectral Accuracy and Resolution

According to an embodiment of the invention, the evaluation of theability of the system to resolve spectral features of an acquired imageshould be established prior to the use of spectral unmixing algorithms.The method for such evaluation uses a long-wavelength pass filter with apredetermined cut-off (for example a filter with a cut-off at about409-nm for collection of light between about 409 nm and 900 nm).Preferably, the determination of spectral accuracy and resolution iscarried out with the use of a temperature-controlled source of light,because the temperature variations may affect the spectral positions ofelemental spectral lines.

A spectral data set (a multispectral image cube similar to that of FIG.1A) is acquired using standardized exposure time and levels ofillumination at high (spectral) resolution settings of the imagingspectrometer using illumination distributed to evenly cover the areacaptured by, for example, a 2D camera array. Such illumination may beprovided from a closed-loop stabilized Hg metal-halide lamp, inreflection from the chosen surface (for example, with the use of a setupsimilar to that of FIG. 4A). An example of the acquired spectrum fromsuch an Hg-doped, metal-halide lamp technology is shown in FIG. 7A.Spectral positions of the elemental spectral peaks of the Hg-lamp areknown (436 nm, 546 nm, and 578 nm).

In accordance with a method 1300 of the present invention, shown in FIG.7C, the spectral features of an image data set are compared to anexpected set of spectral features for an image data set of a knownobject, for example a partially reflective slide. This method ofcharacterization employs spectral peaks of a known standard to determineif the imaging spectrometer recognizes or detects the peak locations atthe wavelengths known to correspond to these elemental spectral peaks.Because the used elemental spectral peaks are known to be very narrow(due to the elemental luminescence properties) and, therefore, can beconsidered approximately spectrally distinct or discreet, the peaks canbe used to determine the ability of the measurement equipment to resolveclosely spaced peaks based on a chosen resolution criteria. (Suchdetermination is based on the assumption that standards such aselemental peaks have much narrower spectral features than the resolutionof the used spectrometer.) Therefore it can be deduced that the peakshape produced by the imaging spectrometer represents the limits of thespectrometer resolving power under the conditions of the test.

In step 1302, a spectrum source with known spectral features isactivated, for example, a light source, and spectrum is output (e.g.,illumination). In step 1303, an image is acquired of the object. In step1304, we average the spectral information (trace), for areas known to behomogenous in spectral properties in order to minimize the impact ofnoise on the spectra measured. In step 1305, the location of thespectral peaks is identified and/or measured from a plot of intensity asa function of wavelength, and compared to known values of where thosepeaks should occur based on knowledge of the spectral features, (forinstance elemental properties of the illumination standard). If thepeaks are offset from the expected locations, then the instrument mayneed adjustment or service, for example, by adjusting the hardwareand/or software associated with the system 500. In an exemplaryembodiment of the present invention, adjustment of the system 500, inresponse to the offset spectral peaks, involves adjustment of wavelengthmapping to recorded intensity values by altering constants used in thespectral image processing and analysis software.

In reference to FIG. 7B, the location of each spectral peak can bedescribed as the wavelength value half-way between rising and fallingintensity values at half of maximum above baseline; this convention fordetermination of a spectral peak location is used to reduce potentialfor misstating the peak location due to aliasing error introduced by thelocation of spectral intensity sample points across the spectral capturerange.

In an exemplary embodiment of the present invention, the resultantspectral data (recorded intensity as a function of wavelength) isidentified, via, for example a plot, and the width of the spectral peaksis identified and/or measured via the plot. In an exemplary embodimentthe measurement is taken approximately halfway between the baseline ofthe peak and the top of each the peak. Typically the spectral featuresof the chosen calibration standard (e.g. Hg elemental peaks) are muchnarrower that the limited resolution of the spectral imaging device.Accordingly, the recorded width of the spectral peaks is identified, andsuch width corresponds to the spectral resolution for a particular partof the wavelength range. Shown in FIG. 7D is a method 1400 for verifyingspectral resolution at each wavelength is according to specification forparticular instrument. In step 1402, a spectrum source with discretefeatures or narrow peaks is activated (for example, a light source withelemental peaks), and spectrum is output onto an object (e.g., atransflective slide). FIG. 7B depicts the spectra acquired with threeresolution settings, plotted and measured using the steps 1404-1406 ofmethod 1400. Since the Full Width at Half Maximum (FWHM) of at leastsome of the spectral peaks of the used calibrated source of spectrum (anHg-lamp in this case) is known to be narrower than the resolution of theinstrument, the acquired spectra are indicative of how the resolutionsetting compares with the known FWHM of the peaks. For example, thecorresponding FWHM values for peaks centered at about 546 nm and 578 nmof can be considered).

Referring further to FIG. 7B, in a 30 nm resolution spectrum (trace C),the Hg-lamp spectral peaks at 546-nm and 578-nm are not resolved, andthe peak intensities are averaged. In a 15 nm resolution spectrum (traceB), the peaks are resolved but defined rather bluntly. In a 10 nmresolution spectrum (trace A), the peaks are well resolved and therelative contribution of each of the peak to the overall spectrum ismore easily discerned through direct inspection of the spectra. Thisexample illustrates the impact that spectral resolution settings canhave on the representation of a sample in the data, and spectralresolution requirements may be measured and specified forinstrumentation using this method.

Spatial Accuracy and Precision/Lateral and Axial Chromatic AberrationsTesting of Optics

Shown in FIG. 8A is a method 1500 utilized to assess imaging of spatialcoordinates, for example, location, and focus quality, of spectralimages taken across a large spectral range. In step 1502, a spectrumsource is activated and spectrum is output to an object, for example aslide with sort of geometric grid (such as a calibration slide having aregular repeating pattern). In an exemplary embodiment of the presentinvention, the spectrum source is a broadband spectrum source, or anysource that generates illumination at wavelengths covering the spectraldetection range. According to an embodiment of the invention, thespatial accuracy and precision of the image acquisition system 502, forexample, a spectral microscope or slide digitization instrument, isevaluated using a precision standard judiciously designed for thispurpose. For example, a reflective pattern standard can be usedproviding that the test pattern is equally visible at all wavelengthsthat are important to a given application (such as multiplex tissueimaging applications). The standard is adapted to produce a set ofregular image features revealing lateral distortions and focal shiftsthat the imaging system may introduce at different wavelengths. In step1503, the image acquisition device 502 is utilized to acquire an imageand/or image data (e.g., a spectral image and/or spectral image data) ofthe slide or object. In step 1504, intensity data from one or more rowsor selections of pixels is identified for each single wavelength orbandwidth in the dataset. In an exemplary embodiment of the presentinvention, the intensity from a single row or column of pixels across animage, at a single wavelength, e.g., blue, is plotted as a function ofspatial position, next another wavelength e.g. green is plotted on thesame graph. This process continues until all of the wavelengths ofinterest are plotted for comparison. Step 1504 may be repeated for aplurality of spatial regions. Periodic spatial features imaged across afield of view at one wavelength may be compared to the same periodicfeatures at another wavelength. Alternatively, the periodic changes maybe compared to the expected performance of the object (e.g., tolerancesof the calibration slide). Alternatively, pixel intensities at aplurality of wavelengths may be iteratively evaluated in the axial (z)axis by taking an average intensity value at a given illuminationwavelength, adjusting the physical focus of the instrument, andreevaluating average intensity values at multiple focal positions. Thisprocedure may be used to determine if there are axial chromatic focalshifts that occur at different wavelengths by finding the focal positionof highest intensity value for a given wavelength (indicating the focusfor the given wavelength and differences in the focal position betweenwavelengths).

FIG. 8B is a one-dimensional (1D) plot 906 of a distribution ofintensity vs. position across the reflective standard 910 of FIG. 8B,acquired at λ₃ In the examples of FIGS. 8B, 8C the reflective standard910 was a carbon film replica standard conventionally used in electronmicroscopy. The intensity values corresponding to edges of the periodicfeatures of the reflective pattern produced by the standard 910 arerepresentative of the lateral spatial resolution of the imaging systemfor in-focus features at a single wavelength from a spectral dataset.

If the reflective standard 910 is placed with a deviation from the“ideal” focus of the optical system 436, the resulting image of suchstandard has decreased image contrast (as acquired from the plot 906)and the spatial resolution of the imaging system determined in relianceon such image contrast according to a defined criterion (for example, arate of intensity change) will be erroneous. The percent deviation ofthe positioning of the reflective standard 910 from the ideal focus atother wavelengths can be approximated by percent reduction in resolutionat the edges of the periodic features of the reflective standard patternas compared to the spatial resolution determined at the chosen referencewavelength, for example) λ₃. The lateral resolution of the MSI system(in this example, the spectrometer or spectral cameral) is furtherdetermined by measuring the relative positions of a half-maximum pointat the curve and the maximum intensity point at the curve and comparingthe wavelengths corresponding to these points. Descriptive metrics, suchas the spatial regularity of image fringes in the plot 906 across thefield-of-view can be determined with appropriate image data processing.

Distortions (such as lateral chromatic distortions, for example) withinthe imaging field can also be determined. A pseudo-color overlay of thewavelength-band images of a spatial calibration pattern should revealgood alignment for all the wavelength components and the spacing betweenregular features should be consistent across the field. Suchspatial/spectral evaluations are necessary to characterize and optimizethe wavelength-dependent performance of an imaging system for assayapplications. For instance, if it becomes clear that there are lateralspatial distortions at some wavelengths, the root cause can beidentified and corrective measures implemented if necessary. If thedistortion situation is not analyzed and/or characterized, the spatiallocalization results for diagnostic applications may be different fordifferent wavelengths recorded in a spectroscopic image and this wouldbe a source of possible error or misinterpretation of molecular-markerlocalization.

Quantum Efficiency, a Wavelength Dependent Response

The quantum efficiency (QE) of the image acquisition apparatus 502(e.g., a photosensitive device, charge-coupled device (CCD) or spectralcamera) may also be determined. Relative quantum efficiency measures theimage acquisition apparatus's 502 sensitivity to light at differentwavelengths. Quantum efficiency refers to the amount of incident photonsthat are converted to electrons and may be represented by a ratio (e.g.,the IPCE ratio). The IPCE ratio correlates to the percentage of photonshitting the photoreactive surface of the image acquisition device 502that produces charge carriers. The IPCE ratio, correlating to quantumefficiency, is measured in electrons per photon or amps per watt.Quantum efficiency may be measured over a range of different wavelengthsto characterize the image acquisition apparatus's 502 relativeefficiency at each wavelength. In an exemplary embodiment of the presentinvention, we determine the quantum efficiency to calibrate for theproportion of photons that actually record (i.e., be sensed), out of allthe photons delivered to the apparatus at different detectionwavelengths. Thus, a user may make corrections to the data based on thequantum efficiency so that differences between instruments or sensorscan be reconciled. In one embodiment, adjustments may be made bycomputational scaling of intensity values in a spectral cube to correctfor differences of QE using different optics. In another embodiment, theexposure time for capture of different wavelength ranges can be changedto compensate for differences in QE. In another embodiment, the QEinformation can be used to increase or decrease the illumination levelto compensate for differences in QE.

To determine a wavelength-dependent response of the imaging system 500,according to an embodiment of the invention several illumination(emission) filters are selected, for example, filters that havesubstantially equal bandwidths corresponding to, for example, a stain orlabel, such as dye analyte (e.g. DAPI) and/or quantum dot emissionwavelengths (for example, a filter with a pass band of about 20 nmcentered at about 460 nm, which is denoted, for simplicity, as 20/460;or a 20/525 filter; or a 20/565 filter, or at least one of 20/585,20/605, 20/625, 20/655, 20/710 filters). The emission filter(s) having,for example, equal or substantially equal bandwidths to cover the entirewavelength range of the system 500, are individually placed in theimaging path shown in FIG. 4A. A power meter or sensor positioned at theobject plane, for example, a surface of the object/sample is used tocalibrate the spectrum source (e.g., a light source), such that thespectrum out may be standardized to output a standardized amountillumination at each wavelength or band being measured. As a result, theamount and/or power of spectrum (e.g., light) may be delivered to orwithin the system 500, or components thereof, and such amount and/orpower of spectrum may be reproduced or substantially reproduced at asurface of the object and/or sample plane to ensure that an equal amountof light is gathered by the imaging optics and guided to the sensor ateach wavelength band of interest A partially reflective sample (forexample, a glass slide 436 as discussed in reference to FIGS. 4A, 4B)may be used to provide a reflective surface, alternatively a transmittedspectrum source 530 (e.g., light source) may be used as long as theoutput can be carefully adjusted and any reflective surface is equallyreflective for all the wavelengths of interest. The image acquisitionapparatus 502 (e.g., spectral camera, spectrometer, etc.) is used, forexample, with standardized spectral resolution setting (e.g., 5 nm) andwith standardized exposure time (e.g., 30 ms) during the acquisition ofall of the quantum efficiency images. Generally, the standardizedexposure time is determined to reach approximately 80% of the saturationlevel of the detector receiving light from the filtered band which hasthe greatest efficiency of detection. As with all data acquired andanalyzed, the images are bias corrected and the mean value is determinedfor each peak wavelength image.

Shown in FIG. 9A is an exemplary method 1600 of determining the quantumefficiency. In step 1602, the spectrum source 522 (e.g., a broadbandlight source) is activated and spectrum (e.g., illumination) is output,and is measurable by a standardized unit (e.g., watts). In step 1603, anarrow band filter from a standardized set of equal bandwidth filterscovering the detection wavelength range of the instrument is selected.In step 1604 the power is measured after light passes through theselected filter and the light source is adjusted to provide astandardized level of light to the object plane. In step 1605, an evenlyilluminated sample plane is imaged by the spectral imaging device. Instep 1606, the spectral image is corrected by subtracting the offset andthe pixel intensities for the entire image are summed to measure thetotal amount of light collected in the spectral image. In step 1607,after all the spectral images have been collected (one for each filter),each pixel-intensity-sum value is divided by the largest sum tonormalize all the sums to a decimal fraction of the largest value. Thelargest sum value represents the wavelength of highest quantumefficiency, and the other values are some fraction of the highestquantum efficiency. In step 1608, these values may be plotted tovisualize the quantum efficiency curve across the wavelength range.Alternatively, the values can be used to generate a calibration curveused to adjust spectral images to negate the different QE of the imagingsystem at different wavelengths. In an exemplary embodiment of thepresent invention, the relative quantum efficiency data generated isutilized to correct recorded intensity values for different wavelengthsacquired with given settings. In an exemplary embodiment of the presentinvention, a quantum efficiency curve is generated and an uncorrecteddataset is divided by the quantum efficiency curve to adjust recordedintensity values for differences in relative detection efficiency atdifferent wavelengths. This process corrects the numerical intensityvalues at each wavelength. In an exemplary embodiment of the presentinvention, such corrected data is utilized to compare data acquiredusing different lens systems, which have different wavelength dependenttransmission properties. In this manner, the imaging system is used tomeasure known amounts of spectrum, for example, light at differentwavelengths/bands, to determine the relative percent efficiency ofdetection at different wavelengths across the spectrum. Such measurementproduces a system-level wavelength dependent efficiency measurement thatincludes both the optics transmission and sensor quantum efficiency. Forinstance, a system may have peak efficiency of detection at wavelength500-nm, with 30% of peak efficiency at wavelength 400-nm and 30%efficiency at wavelength 600-nm. If these values of detection efficiencyare known, then the measurement of analyte intensities taken atdifferent wavelengths (e.g. intensity of light of quantum dot 565 andthat of and quantum dot 655) can be corrected to take this differentefficiency of detection due to the instrument into consideration. Thischaracterization method permits calibration to enable comparison ofmeasurements taken at different wavelengths and for datasets taken usingdifferent components with different transmission efficiencies.

The percentage difference in values measured at different wavelengthscan be compared between instruments or between optical configurations toprovide a comparison of instrument response to wavelength, givenstandardized input (large disparities in wavelength response shouldbecome apparent between devices using this approach). The ability tocorrect for differences in quantum efficiency at different wavelengthspermits accurate interpretation of samples without the potential formisinterpretation of analyte concentration due to the wavelengthefficiency of a given instrument.

Calibration of an MSI according to embodiments of methods and algorithmsof the invention described ensures accurate imaging results insubstantial operational isolation and decoupling of the performance ofthe imaging instrument from variability of fluorescent samples and yetstill provides an integrated system level performance. According tothese embodiments, a calibrated light source and durable physicalstandards can be built in the imaging system and combined with softwaretools to permit routine and, optionally, automated, check andself-calibration procedures and troubleshooting procedures to beperformed.

Once an MSI and optical acquisition system has been calibrated accordingto the methods described above (or to other related methods), it becomespossible for the user of such imaging system to test computer programproducts used in conjunction with the MSI acquisition (such as, forexample, the algorithms embodying the spectral unmixing data processingand algorithms related to data normalization choices such as, forexample, peak normalization, vector normalization, area normalization)that increase fidelity of the data processing. At least for the samereason, the MSI system calibrated independently from a fluorescentstandard is configured to permit a sample-independent verification ofwhether the unmixed spectral data correctly represents the contributionsof multiple fluorescent species. Indeed, by first validating theinstrumental performance and calibration, the user can isolate andidentify other sources of errors that may be related to samplepreparation and/or the software processing algorithms. If the dataprocessing algorithms have been calibrated and/or verified independentlyfrom a particular fluorescent standard and shown to deliver physicallyaccurate results, then the deviation of the results of spectral unmixingof multispectral images from what is physically accurate is indicativeof changes of or deviations in operational performance of the MSI systemitself.

Embodiments of methods permitting such sample-independent imaging dataverification are further discussed below.

Verification of a Quantitative Multiplex Spectral-Unmixing

For a fluorophore standard such as a wet mount of fluorescent dye inknown concentration, or fluorescent polystyrene beads, the relativesignal contribution of an analyte depends on the relative output of thespectrum source, for example, a light source, at different wavelengthsand the optical properties of the image forming apparatus 508 and/orimage acquisition apparatus 502, (e.g., microscope); however, this isnot widely appreciated. For this reason, a fluorophore standardvalidated using one instrument may be completely useless as a referenceon a different instrument. Moreover, fluorophore standards are notuseful for spectral instrument calibration when other reporters, such asquantum dots, are used because the excitation wavelengths and filtersused are completely different. In the novel method described here, theimpact of sample properties is almost non-existent, and the instrumentis measured against reproducible illumination. Instruments that areequipped to identical standards will be expected to performequivalently, and the impact of changing different components on theexpected outcome can be measured.

According to an embodiment of the invention, the verification of methodsof spectral unmixing generally makes use of a dual-beam spectrum sourceand/or illumination geometry (e.g., spectrum sources 522 and 530, asshown in FIG. 5A) configured to deliver spectrum (e.g., illumination) atmultiple wavelengths/bands (having various intensity peaks at multiplewavelengths). In an exemplary embodiment of the present invention, aspectrally-selective system 528, for example, as shown in as shown inFIG. 5A, may be placed in the path of spectrum output from each of thespectrum sources 522 and 530. In an exemplary embodiment of the presentinvention, each spectrally-selective system 528 has different band passspecifications. As a result, two beams of light are generated, with eachhaving its own spectral features, for example, their own distinctspectral features, (such as, wavelength, intensity, etc.).

The two beams mix at, a plane or surface, for example, the object plane524, where the imaging acquisition apparatus 502 is focused. The objectplane 524 corresponds to a plane of a substrate, material, or substance,for example, a clean glass slide, or a stage, for example, a microscopestage. In exemplary embodiments of the present invention, the glassslide is partially reflective and partially transmissive. Thus, part ofthe incident beam is reflected from the partially reflective surface ofthe glass slide, and part of the transmitted beam passes through theglass slide and is mixed with the reflected portion of the light. Bycarefully controlling and standardizing the amount of input light, thetwo sets of spectral features can be controlled and held to a precisespecification.

The relative contributions from the different peaks (i.e., the peaks ofthe light signal reflected from the sample plane and the peaks of thespectrum signal (e.g., light signal) of the transmitted spectrum (e.g.,light)) can be modulated, and thus, the two sets of peaks can beconvolved/mixed to test an imaging system and/or instrument's, forexample, ability to unmix overlapping spectra. Because each of the twospectrum sources and their output amounts, intensities, and/orwavelengths (e.g., light sources) can be controlled independently, therelative peak contributions to the convolved signal can be unambiguouslydetermined or pre-determined before the spectra from the two spectrumsources are mixed.

Also, because each of the two spectrum sources (e.g., light sources) canbe controlled independently, the contributing integrated intensity ofpeaks attributed to particular bandwidths may be attenuated and/orincreased and/or decreased to test the unmixing in the context of theentire dynamic range of the imaging system 500 and/or image acquisitionapparatus 502, or components thereof (e.g., sensors, detectors, ordetection system). Because of the controlled specifications of thespectrum (e.g., illumination) and sensor systems, differences in theunmixing results (i.e., between the expected contributions of spectrafrom the spectrum sources and the unmixing results from an imagingsystem's unmixing algorithms) may be indicative of a change to one ormore properties of the MSI system or components thereof. The tolerancesfor instrument performance are thus isolated from samples (e.g.,biological specimens and/or tissue slides), and any instrumenttolerances may be adjusted to a well-defined specification.

An example of such system has been shown in FIG. 4A. Generally, the twoincident beams 426, 446 overlap at the site of an object, for example, asample plane provided, for example, by the clean transflective glassslide 430. The relative levels of illumination (for example, irradiance)provided by these beams can generally be varied instrumentally, and thusthe spectra of the beams 426, 446 can be mixed to test the ability ofthe system 500, or component thereof, for example, the image acquisitionsystem 502, to unmix these overlapping spectra across the entire dynamicrange of an optical detector (already calibrated according to one ormore of embodiments of the invention discussed in reference to FIGS. 5-9). Because each of the two sources of spectrum, for example, lightsources (the source 410 and the source of light 448) can be controlledindependently, the relative contributions of the beams 426, 446 to thesignal, for example, optical signal, received by the image acquisitionsystem can also be unambiguously determined before the spectra of thebeams 426, 446 are mixed.

Because the illumination geometry of an embodiment ensures even fieldillumination, the detection response across the entire aperture of thedetector (e.g. image acquisition apparatus 502, or sensors thereof) canbe verified and deviation of responses from different pixels of thedetector, or from the image acquisition device's expected performance orperformance specifications may be determined. In a related embodiment,an object, for example, a sample having non-uniform spatial distributionof reflectance and/or transmittance could be used instead of the glassslide 430 to ensure different ratios of spectral peaks' contributiondifferent spatial coordinates of an image detected by the imageacquisition apparatus 502 during a single image and/or data acquisitioncycle.

For a single beam of spectrum, for example, a beam illuminating light(for example, the incident beam 426, the spectrum of which is shown inFIG. 4B), each of the n spectral peaks is analogous to the spectra of asingle fluorescent marker (for example, a quantum dot) for the purposesof testing the spectral unmixing procedure.

Because the n spectral peaks are defined by physical properties of thechosen spectrally selective system 410 a, such as a band-pass filter,the spectral positions of these peaks are expected to remain unchangedunless the alignment of the filter 410 a is changed. (It is appreciatedthat the spectral locations of the transmission peaks of different unitsof the bandpass filter 410 a made to the same specification are subjectto a measurable tolerance error.)

In one embodiment, the optical acquisition system is appropriatelyadapted to ensure that a detector of the system is below saturationlevel (for example, within 80% of the saturation level) when either thesource 410 or both the source 410 and the source of light 446 (i.e.,spectrum source 448) are switched on. Such illumination limit isenabled, for example, by using stabilized light source(s) calibrated toreliably reproduce (for example, within E %=1% error) illuminationlevels in terms of known units (e.g., mW) at the sample plane.

Referring to FIG. 10 , an embodiment 1700 of a method for verificationof a process of spectral unmixing of the relative contributions from themultiple spectral peaks of a calibrated light input includes adetermination of the overall spectral power, (e.g., optical power)received by the detector or detectors of the image acquisition system502 and/or image forming apparatus 508 s. The overall spectrum power,for example, optical power, is proportional to an area under thespectral curve 422, or to intensity of the source 410 that is spectrallyintegrated. To determine the integrated intensity, image acquisitionsystem 502 and/or image forming apparatus, for example, a microscope, isfirst focused on the reflective surface of the slide 430 and amultispectral image of the evenly or substantially even illuminatedfield of the slide 430 is acquired using a single light path (in thiscase, the light path in reflection), at step 1010.

The resulting multispectral image is corrected, at step 1020, to takeinto account the offset of the signal from a baseline intensity value ofzero. This offset-correction procedure is carried out in a fashionsimilar to that described in reference to FIG. 4A and substantiallyincludes a) collecting a multispectral image under the same acquisitionconditions but with no light from the spectrum sources 400 and/or 448,for example, optical source(s) delivered to the detector and at a zeroexposure time; (b) determining a signal level as an mode intensity; and(c) subtracting the determined signal level that represents a signaloffset and/or a pre-determined constant, from the entire multispectraldata set corresponding to the earlier acquired multispectral image, on apixel-by-pixel basis, and at every wavelength used in image acquisition.

The mode intensity is derived at every wavelength in a spectral datasetand can be saved as a one-dimensional array (spectral trace) for use inprocessing all data acquired under given settings. In reference to step1705 of FIG. 10 and FIG. 11 , the integrated intensity corresponding tothe area 1106 under the spectral trace envelope 1110 is furtherdetermined. The integrated intensity represents the sumwavelength-integrated intensities (of the image) delivered by allspectral bands of the spectrum (e.g., light) beam that has beengenerated by the source 410 and reflected off of the slide 430 and isutilized to derive a quantity that represents the total amount of lightrecorded for the multi-band illumination.

In reference to FIG. 12 and step 1706 of FIG. 10 , to determine therelative contribution of each of the spectral bands Bi, B2, B3, and B4to the overall signal, the area under the trace 1110 within each of thebands is calculated and expressed as a percentage of the total area1106. (Data processing performed at step 1706 is analogous to measuringindividual intensity contributions of several distinct fluorophores orquantum dots.) The different relative intensity contributions to theoverall signal provide a reproducible (to within E % error, asestablished by a calibrated light source) and well-characterizedstandard to test spectral unmixing performance. Accordingly, once therelative contributions of the individual spectral bands of the multibandcalibration source and/or spectrum source 410 have been established atstep 1706, the information can be used to test an algorithm's ability toreconstruct the measured intensities from subsequent spectralacquisitions using these settings. The optical properties of suchstandards are known because they are measurable directly, as discussedabove.

In further reference to FIG. 10 and referring now to FIG. 13 , datarepresenting the spectral trace 1110 can be optionally processed to formnormalized reference spectrum for each of the bands Bi, B2, B3, and B4of the multiband calibration and/or spectrum source 410. To this end, aportion of the data set representing a portion of the spectral trace1110 that corresponds to a given band is separated or “clipped” at step1706 from the remaining data (for example, at a point midway betweenadjacent spectral peaks), and corresponding to the separated band arethen normalized at step 1707. Typically in the form of normalizationchosen equalizes the integrated area under the curve for each separatedband to be the same as the area under the curve for the spectral peakrepresenting the largest contribution to the overall signal. Theresulting individual reference spectra S₁, S₂, S₃, and S₄ (which in someembodiments is equalized) of the individual bands B₁, B₂, B₃, and B₄ canbe now used in linear unmixing data processing to separate spectralcontributions of different pass bands. The use of these spectra S₁, S₂,S₃, and S₄ (e.g., equalized spectra) as reference spectra during thespectral unmixing of spectra, (e.g., light) from a known combination ofpass bands facilitates the calculation of the relative contribution ofeach pass band. This same principle can be employed when tissue labeledwith fluorescent analytes is imaged, for example, in quantization ofintensity values when ratios of intensity contributions hold importantinformation about underlying protein or gene expression.

Verification of Quantitative Unmixing Algorithm for a Single Light Path.

Because, as was discussed above, the relative intensity contributions(shown in FIG. 12 ) of separate bands Bi, B2, B3, and B4 of thecalibration light source 410 into the overall spectrum (e.g., light)input received by the image acquisition apparatus 502 have been measureddirectly, the normalized reference spectra S1, S2, S3, and S4 of FIG. 13can now be used in linear unmixing to ensure that the linear unmixingalgorithm is not erroneous. (In a related embodiment, the proposedmethodology is similarly applicable non-linear unmixing.) In particular,if the linear unmixing algorithm is processing data without errors, arelative intensity contribution of a given band determined with the useof the unmixing algorithm would be consistent with the correspondingdirectly-measured intensity contribution of FIG. 12 . Table 1, forexample of the application of this method, presents comparison among theresults of spectral unmixing performed with two software implementedalgorithms, SpectraView and Specounter (both being trademarks of AppliedSpectral Imaging, Inc.) which were evaluated for application tomultiplex tissue diagnostics using the same standardized imaginginstrument hardware, and the directly measured calibrating data of FIG.12 (referred to as Actual). As shown, the unmixing algorithms ensuresubstantial accuracy of the calculation to within about 5% of theoverall 100% of summed intensities. FIG. 14 provides correspondingillustrations including a bar diagram. In the ideal case, the spectraldistribution of spectrum (e.g., light) from a source of spectrum (e.g.,light) such as the spectrum and/or calibration source 410 in reflectionoff of the object/slide 430 should remain unchanged regardless of thepower level of the spectrum (e.g., light) output at different spectrum(e.g., light) levels because the spectrum (e.g., light) output from thesource 410 is varied by, for example, a chromatically neutral mechanism416 while keeping the power feed to the source 410 constant. Similarlythe spectral distribution of acquired spectrum (e.g., light does notdepend on the duration of acquisition time (i.e., exposure time).

TABLE 1 SpectraView Unmixed Channels Specounter Unmixed Channels AverageStd Average Std. Standard Intensity Dev. % Total Standard Intensity Dev.% Total Actual Peak 1 25786 1309 6.9% Peak 1 4657 294 5.4%  5% Peak 291709 3581 24.5% Peak 2 21527 965 24.8% 21% Peak 3 249731 10662 66.6%Peak 3 59918 2687 68.9% 70% Peak 4 7519 925 2.0% Peak 4 820 226 0.9%  4%Sum 374745 Sum 86922Verification of Quantitative Unmixing Algorithm for Multiple LightPaths.

It is understood that verification of accuracy of a spectral-dataunmixing algorithm can be similarly carried out when spectrum, e.g.,light, is delivered to the image acquisition apparatus 502 alongmultiple paths. Accordingly, a multi-path verification procedurerequires the use of different calibration sources in different paths.Referring to FIG. 15A, 15B, 15C, for example, two light portions thathave interacted with the object/slide 430 are received by the opticalacquisition system: the beam 440, which is a portion of the beam 426reflected by the slide 430, and a beam 1510, which is a portion of thebeam 446 produced by a second spectrum 1514 and transmitted throughslide 430. The second spectrum/calibration source of light 1514 isconfigured similarly to the source 410 in that it contains a stabilizedcalibrations spectrum (e.g., light) emitter, a calibration multi-bandpass filter 1514 a and a diaphragm (not shown) at the output of thesource 410 and illuminates the field of view evenly or substantiallyeven. A spectral distribution of spectrum (e.g., light) 446 generallydiffers from that of spectrum (e.g., light) 414 shown in FIG. 15A. Anexample of the spectral distribution 1520 of spectrum (e.g., light) 446,shown in FIG. 15B, contains 3 bands: B₅, B₆, and B₇ centered atrespectively corresponding wavelengths λ₅, λ₆, and λ₇.

Relative contributions of optical power received in each of the bandsB5, B6, and B7 (as compared to the total spectrum (e.g., optical) powerof the transmitted beam 446) can be measured directly when only thesource 1514 is turned on and the source 410 is turned off. Accordingly,reference spectra for transmitted spectrum (e.g., light) is definedaccording to a method discussed in relation to FIG. 13 .

It is also appreciated that the reflected beam 440 (having spectralbands B₁, B₂, B₃, and B₄) and the transmitted beam 1510 (having spectralbands B5, B6, and B7) substantially do not interfere and do overlaplinearly at the detector or detectors (e.g., sensors) of the imageacquisition system 502. Consequently, when both spectrum (e.g., light)sources 410, 1514 are turned on, spectrum power (e.g., optical power)delivered to the image acquisition system 502, in each of theabovementioned bands, can be measured directly and independently of thatin another band in either reflected or transmitted spectrum (e.g.optical) paths, thereby permitting direct measurement of thecontribution of spectrum (e.g., optical) power in each of the spectralbands registered at the detector relative to the total received spectrum(e.g., optical) power. FIGS. 15B and 15C illustrate, for comparison,spectra 1520 and 1530 of spectra (e.g., light) beams 414 and 440,respectively, and, for example, to the area under the strongest peak B₇,λ₇. Here, the halogen lamp was used as the light source 1514 and thefilter 1514 a included an optical filter transmitting in near IR.

In reference to FIGS. 16A, 16B, spectral characteristics of spectrum,e.g., light, received by the detector from either of the individualoptical paths (i.e., in reflection and transmission) are furtherdirectly measured as discussed above and used to construct referencespectral calibration standards (similar to those of FIG. 13 ) forcalibration/verification of spectral unmixing system and algorithms.Normalized spectra of calibration standards so devised for bothreflection and transmission paths (e.g., optical paths) are plottedtogether in FIG. 17 , showing substantial overlap of spectra of thetransmission and reflection paths' calibration sources 1514, 410 in thevisible portion of the spectrum.

The “aggregate” normalized spectral trace 1810 of FIG. 18 represents thespectral trace registered by the detector of the image acquisitionsystem when both individual calibration/reference light standards 410and 1514 are switched on. The area 1916 under the spectral traceenvelope 1810 may be further determined, as shown in FIG. 19 , andcompared with the sum of the areas under the individual spectral tracesof FIGS. 16A and 16B to determine agreement between the individualcomponents' spectral traces and the total. Because the opticalacquisition system was earlier referenced to the normalized calibrationspectra 1520, 1530, the integrated intensity 1916 remains substantiallyequal to the sum of intensities of the individual spectrum (e.g., light)standards 410, 414 as long as the overall optical train (including thefilters, lenses, and optical acquisition system itself) does notexperience any changes such as re-alignment or replacement, for example.A substantial deviation from such balance is indicative that the opticaltrain of the MSI system has been changed since the moment of calibrationusing individually operating sources 410,414.

FIG. 20 provides an illustration to a system-level test of a measurementsystem that is assumed to have been pre-calibrated. In reference to thelight received at the detector component of the measurement systemrepresents a mix of six at least partially overlapping spectral bandseach of which represents a spectral standard. (Bands of a spectralstandard are emulated by using two spectral filters, each in one arm ofthe measurement system of the invention, with known spectralcharacteristics.) Therefore, it is known a priori the amount of spectrum(e.g., light) signal present in the mix at each of the spectral bandsand/or wavelengths. As can be seen from the inset A of FIG. 20 ,different spectral bands/channels overlap in different ratios, and theassumption is made that no non-linear effects affect the spectral mixingof spectra, e.g., light, incident onto the detector. An embodiment of aspectral unmixing algorithm is used to determine, via calculation,values representing the amount of spectrum, e.g., light, in eachspectral bands (see insets B and C). The comparison between the knownactual and calculated values indicates whether the used spectralunmixing algorithm requires a correction and to what degree.

FIGS. 21A and 21B provide plots and related data illustrating a spectralunmixing, according to the method described above, of 9 spectralfeatures with accuracy to within 5%, as measured with respect to theknown contribution for individual spectral peaks of a known standard.

FIG. 22 and the related data in Table 2 illustrate efficiency ofoperation of an embodiment of the invention used for spectral unmixingemploying 4-band standard filters (e.g., optical filters) in incidentand transmitted beams. Table 2 presents values corresponding to relativeintensity contributions for the 8 spectral peaks as percentage of thetotal intensity of light received by the system in the process offorming a hyperspectral cube of data. As the “Difference” columnindicates, the results of linear unmixing obtained with an embodiment ofthe algorithm of the invention are in good agreement with valuesmeasured directly.

TABLE 2 ASI Spectral Counter Unmix Layers Area Under Curve Area MeanStdDev Mode % total Measured % Difference RPeakl 898560 1643.258 119.7781633.832 0.83% 1.10% 0.27% RPeak2 898560 15632.04 885.115 16067.12 8.16%5.10% −3.06% RPeak3 898560 55912.92 3144.496 58180.38 29.56% 27.80%−1.76% RPeak4 898560 26112.59 1794.595 27189.74 13.81% 17.40% 3.59%TPeakl 898560 1985.762 199.211 1949.101 0.99% 1.60% 0.61% TPeak2 89856020366.96 1166.834 19537.53 9.93% 9.30% −0.63% TPeak3 898560 60849.472895.438 58198.08 29.57% 31.90% 2.33% TPeak4 898560 14689.91 720.8614087.45 7.16% 10.70% 3.54% Total 196843.2 100.00%

FIG. 23 illustrates a generalized example of a suitable computing systemin which several of the described innovations may be implemented. Thecomputing system is not intended to suggest any limitation as to scopeof use or functionality, as the innovations may be implemented indiverse general-purpose or special-purpose computing systems.

With reference to FIG. 23 , the computing system includes one or moreprocessing units and memory 2320, 2325. The processing units 2315executes computer-executable instructions. A processing unit can be ageneral-purpose central processing unit (CPU), processor in anapplication-specific integrated circuit (ASIC) or any other type ofprocessor. In a multi-processing system, multiple processing unitsexecute computer-executable instructions to increase processing power.For example, FIG. 23 shows a central processing unit 2310 as well as agraphics processing unit or co-processing unit 2315. The tangible memory2320,2325 may be volatile memory (e.g., registers, cache, RAM),non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or somecombination of the two, accessible by the processing unit(s). The memory2320,2325 stores software 2380 implementing one or more innovationsdescribed herein, in the form of computer-executable instructionssuitable for execution by the processing unit(s).

A computing system may have additional features. For example, thecomputing system includes storage 2340, one or more input devices 2350,one or more output devices 2360, and one or more communicationconnections 2370. An interconnection mechanism (not shown) such as abus, controller, or network interconnects the components of thecomputing system. Typically, operating system software (not shown)provides an operating environment for other software executing in thecomputing system, and coordinates activities of the components of thecomputing system.

The tangible storage 2340 may be removable or non-removable, andincludes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, orany other medium which can be used to store information in anon-transitory way and which can be accessed within the computingsystem. The storage 2340 stores instructions for the software 2380implementing one or more innovations described herein.

The input device(s) 2350 may be a touch input device such as a keyboard,mouse, pen, or trackball, a voice input device, a scanning device, oranother device that provides input to the computing system. For videoencoding, the input device(s) 50 may be a camera, video card, TV tunercard, or similar device that accepts video input in analog or digitalform, or a CD-ROM or CD-RW that reads video samples into the computingsystem. The output device(s) 2360 may be a display, printer, speaker,CD-writer, or another device that provides output from the computingsystem.

The communication connection(s) 2370 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context ofcomputer-readable media. Computer-readable media are any availabletangible media that can be accessed within a computing environment. Byway of example, and not limitation, with the computing system,computer-readable media include memory 2230, 2325, storage 2340, andcombinations of any of the above.

The innovations can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing system on a target real orvirtual processor. Generally, program modules include routines,programs, libraries, objects, classes, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. The functionality of the program modules may be combined or splitbetween program modules as desired in various embodiments.Computer-executable instructions for program modules may be executedwithin a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unlessthe context clearly indicates otherwise, neither term implies anylimitation on a type of computing system or computing device. Ingeneral, a computing system or computing device can be local ordistributed, and can include any combination of special-purpose hardwareand/or general-purpose hardware with software implementing thefunctionality described herein.

For the sake of presentation, the detailed description uses terms like“determine” and “use” to describe computer operations in a computingsystem. These terms are high-level abstractions for operations performedby a computer, and should not be confused with acts performed by a humanbeing. The actual computer operations corresponding to these terms varydepending on implementation.

Any of the computer-readable media herein can be non-transitory (e.g.,memory, magnetic storage, optical storage, or the like).

Any of the storing actions described herein can be implemented bystoring in one or more computer-readable media (e.g., computer-readablestorage media or other tangible media).

Any of the things described as stored can be stored in one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media).

Any of the methods described herein can be implemented bycomputer-executable instructions in (e.g., encoded on) one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media). Such instructions can cause a computer to perform themethod. The technologies described herein can be implemented in avariety of programming languages.

Any of the methods described herein can be implemented bycomputer-executable instructions stored in one or more computer-readablestorage devices (e.g., memory, magnetic storage, optical storage, or thelike). Such instructions can cause a computer to perform the method.

While the invention is described through the above-described examples ofembodiments, it will be understood by those of ordinary skill in the artthat modifications to, and variations of, the illustrated embodimentsmay be made without departing from the inventive concepts disclosedherein. For example, although some aspects of embodiments have beendescribed with reference to a flowchart, those skilled in the art shouldreadily appreciate that functions, operations, decisions, etc. of all ora portion of each block, or a combination of blocks, of the flowchartmay be combined, separated into separate operations or performed inother orders. Moreover, while the embodiments are described inconnection with various illustrative data structures, one skilled in theart will recognize that the system may be embodied using a variety ofdata structures. Furthermore, disclosed aspects, or portions of theseaspects, may be combined in ways not listed above. A computer programproduct effectuating a programmable processor of a system to perform thesteps of embodiments of the algorithm described in this application isalso within the scope of the invention. Accordingly, the inventionshould not be viewed as being limited to the disclosed embodiment(s).

What is claimed:
 1. A method comprising: generating, using an imagingsystem, a first set of image data corresponding to a tissue sample,wherein the first set of image data is generated based on one or moreimages captured by the imaging system at a maximum light level of adynamic range indicated by a sensor of the imaging system at apredetermined exposure time; generating, using the imaging system, asecond set of image data corresponding to the tissue sample, wherein thesecond set of image data is generated based on one or more imagescaptured by the imaging system at a minimum light level of the dynamicrange indicated by the sensor of the imaging system at the predeterminedexposure time; generating, using the imaging system, a third set ofimage data corresponding to the tissue sample, wherein the third set ofimage data is generated based on one or more images captured by theimaging system at a medium light level within the dynamic rangeindicated by the sensor at the predetermined exposure time, and whereinthe medium light level is between the maximum light level and theminimum light level; determining a linear regression value for each ofthe first, second, and third sets of image data; identifying, based onthe determined linear regression values, an estimated degree oflinearity corresponding to the imaging system, wherein the estimateddegree of linearity indicates a ratio between signal output of theimaging system and an amount of light received by the imaging system;and calibrating one or more components of the imaging system based atleast in part on the estimated degree of linearity.
 2. The method ofclaim 1, wherein: a first image of the one or more images captured bythe imaging system at the maximum light level is captured at a firstpredetermined wavelength; and a second image of the one or more imagescaptured by the imaging system at the maximum light level is captured ata second predetermined wavelength, wherein the first predeterminedwavelength is different from the second predetermined wavelength.
 3. Themethod of claim 1, further comprising: identifying a first set of pixelintensity values from the first set of image data; determining a firstset of statistical data of the first set of pixel intensity values; anddetermining, based on the first set of statistical data, the linearregression value for the first set of image data.
 4. The method of claim3, wherein determining the linear regression value for the first set ofimage data further comprises: generating a graph corresponding to thefirst set of image data, wherein: a first axis of the graph indicates avariance value of the first set of statistical data; and a second axisof the graph indicates a mode value of the first set of statisticaldata.
 5. The method of claim 1, wherein determining the linearregression value for each of the first, second, and third sets of imagedata further comprises generating a conversion value for each of thefirst, second, and third sets of image data, wherein the conversionvalue indicates an estimated number of electrons recorded by the sensorof the imaging system at each pixel of a respective image.
 6. The methodof claim 1, wherein the sensor is a charge-coupled device sensor thatconverts photons characterized as an analog signal to an digital signal.7. The method of claim 1, wherein the dynamic range indicates a ratio ofmaximum and minimum light intensity values that the imaging system iscapable of converting from an analog signal to a digital signal.
 8. Asystem comprising: a processing unit comprising one or more processors;and memory coupled with and readable by the processing unit and storingtherein a set of instructions which, when executed by the processingunit, causes the one or more processors to perform operationscomprising: generating, using an imaging system, a first set of imagedata corresponding to a tissue sample, wherein the first set of imagedata is generated based on one or more images captured by the imagingsystem at a maximum light level of a dynamic range indicated by a sensorof the imaging system at a predetermined exposure time; generating,using the imaging system, a second set of image data corresponding tothe tissue sample, wherein the second set of image data is generatedbased on one or more images captured by the imaging system at a minimumlight level of the dynamic range indicated by the sensor of the imagingsystem at the predetermined exposure time; generating, using the imagingsystem, a third set of image data corresponding to the tissue sample,wherein the third set of image data is generated based on one or moreimages captured by the imaging system at a medium light level within thedynamic range indicated by the sensor at the predetermined exposuretime, and wherein the medium light level is between the maximum lightlevel and the minimum light level; determining a linear regression valuefor each of the first, second, and third sets of image data;identifying, based on the determined linear regression values, anestimated degree of linearity corresponding to the imaging system,wherein the estimated degree of linearity indicates a ratio betweensignal output of the imaging system and an amount of light received bythe imaging system; and calibrating one or more components of theimaging system based at least in part on the estimated degree oflinearity.
 9. The system of claim 8, wherein: a first image of the oneor more images captured by the imaging system at the maximum light levelis captured at a first predetermined wavelength; and a second image ofthe one or more images captured by the imaging system at the maximumlight level is captured at a second predetermined wavelength, whereinthe first predetermined wavelength is different from the secondpredetermined wavelength.
 10. The system of claim 8, wherein the memorystores additional instructions which, when executed by the processingunit, causes the one or more processors to perform operationscomprising: identifying a first set of pixel intensity values from thefirst set of image data; determining a first set of statistical data ofthe first set of pixel intensity values; and determining, based on thefirst set of statistical data, the linear regression value for the firstset of image data.
 11. The system of claim 10, wherein determining thelinear regression value for the first set of image data furthercomprises: generating a graph corresponding to the first set of imagedata, wherein: a first axis of the graph indicates a variance value ofthe first set of statistical data; and a second axis of the graphindicates a mode value of the first set of statistical data.
 12. Thesystem of claim 8, wherein determining the linear regression value foreach of the first, second, and third sets of image data furthercomprises generating a conversion value for each of the first, second,and third sets of image data, wherein the conversion value indicates anestimated number of electrons recorded by the sensor of the imagingsystem at each pixel of a respective image.
 13. The system of claim 8,wherein the sensor is a charge-coupled device sensor that convertsphotons characterized as an analog signal to an digital signal.
 14. Thesystem of claim 8, wherein the dynamic range indicates a ratio ofmaximum and minimum light intensity values that the imaging system iscapable of converting from an analog signal to a digital signal.
 15. Acomputer-program product tangibly embodied in a non-transitorymachine-readable storage medium, including instructions configured tocause one or more data processors to perform operations comprising:generating, using an imaging system, a first set of image datacorresponding to a tissue sample, wherein the first set of image data isgenerated based on one or more images captured by the imaging system ata maximum light level of a dynamic range indicated by a sensor of theimaging system at a predetermined exposure time; generating, using theimaging system, a second set of image data corresponding to the tissuesample, wherein the second set of image data is generated based on oneor more images captured by the imaging system at a minimum light levelof the dynamic range indicated by the sensor of the imaging system atthe predetermined exposure time; generating, using the imaging system, athird set of image data corresponding to the tissue sample, wherein thethird set of image data is generated based on one or more imagescaptured by the imaging system at a medium light level within thedynamic range indicated by the sensor at the predetermined exposuretime, and wherein the medium light level is between the maximum lightlevel and the minimum light level; determining a linear regression valuefor each of the first, second, and third sets of image data;identifying, based on the determined linear regression values, anestimated degree of linearity corresponding to the imaging system,wherein the estimated degree of linearity indicates a ratio betweensignal output of the imaging system and an amount of light received bythe imaging system; and calibrating one or more components of theimaging system based at least in part on the estimated degree oflinearity.
 16. The computer-program product of claim 15, wherein: afirst image of the one or more images captured by the imaging system atthe maximum light level is captured at a first predetermined wavelength;and a second image of the one or more images captured by the imagingsystem at the maximum light level is captured at a second predeterminedwavelength, wherein the first predetermined wavelength is different fromthe second predetermined wavelength.
 17. The computer-program product ofclaim 15, wherein the instructions are further configured to cause theone or more data processors to perform operations comprising:identifying a first set of pixel intensity values from the first set ofimage data; determining a first set of statistical data of the first setof pixel intensity values; and determining, based on the first set ofstatistical data, the linear regression value for the first set of imagedata.
 18. The computer-program product of claim 17, wherein theinstructions are further configured to cause the one or more dataprocessors to perform operations comprising: generating a graphcorresponding to the first set of image data, wherein: a first axis ofthe graph indicates a variance value of the first set of statisticaldata; and a second axis of the graph indicates a mode value of the firstset of statistical data.
 19. The computer-program product of claim 15,wherein determining the linear regression value for each of the first,second, and third sets of image data further comprises generating aconversion value for each of the first, second, and third sets of imagedata, wherein the conversion value indicates an estimated number ofelectrons recorded by the sensor of the imaging system at each pixel ofa respective image.
 20. The computer-program product of claim 15,wherein the sensor is a charge-coupled device sensor that convertsphotons characterized as an analog signal to an digital signal.